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Bender RG, Sirota SB, Swetschinski LR, Dominguez RMV, Novotney A, Wool EE, Ikuta KS, Vongpradith A, Rogowski ELB, Doxey M, Troeger CE, Albertson SB, Ma J, He J, Maass KL, A.F.Simões E, Abdoun M, Abdul Aziz JM, Abdulah DM, Abu Rumeileh S, Abualruz H, Aburuz S, Adepoju AV, Adha R, Adikusuma W, Adra S, Afraz A, Aghamiri S, Agodi A, Ahmadzade AM, Ahmed H, Ahmed A, Akinosoglou K, AL-Ahdal TMA, Al-amer RM, Albashtawy M, AlBataineh MT, Alemi H, Al-Gheethi AAS, Ali A, Ali SSS, Alqahtani JS, AlQudah M, Al-Tawfiq JA, Al-Worafi YM, Alzoubi KH, Amani R, Amegbor PM, Ameyaw EK, Amuasi JH, Anil A, Anyanwu PE, Arafat M, Areda D, Arefnezhad R, Atalell KA, Ayele F, Azzam AY, Babamohamadi H, Babin FX, Bahurupi Y, Baker S, Banik B, Barchitta M, Barqawi HJ, Basharat Z, Baskaran P, Batra K, Batra R, Bayileyegn NS, Beloukas A, Berkley JA, Beyene KA, Bhargava A, Bhattacharjee P, Bielicki JA, Bilalaga MM, Bitra VR, Brown CS, Burkart K, Bustanji Y, Carr S, Chahine Y, Chattu VK, Chichagi F, Chopra H, Chukwu IS, Chung E, Dadana S, Dai X, Dandona L, Dandona R, Darban I, Dash NR, Dashti M, Dashtkoohi M, Dekker DM, Delgado-Enciso I, Devanbu VGC, Dhama K, Diao N, Do THP, Dokova KG, Dolecek C, Dziedzic AM, Eckmanns T, Ed-Dra A, Efendi F, Eftekharimehrabad A, Eyre DW, Fahim A, Feizkhah A, Felton TW, Ferreira N, Flor LS, Gaihre S, Gebregergis MW, Gebrehiwot M, Geffers C, Gerema U, Ghaffari K, Goldust M, Goleij P, Guan SY, Gudeta MD, Guo C, Gupta VB, Gupta I, Habibzadeh F, Hadi NR, Haeuser E, Hailu WB, Hajibeygi R, Haj-Mirzaian A, Haller S, Hamiduzzaman M, Hanifi N, Hansel J, Hasnain MS, Haubold J, Hoan NQ, Huynh HH, Iregbu KC, Islam MR, Jafarzadeh A, Jairoun AA, Jalili M, Jomehzadeh N, Joshua CE, Kabir MA, Kamal Z, Kanmodi KK, Kantar RS, Karimi Behnagh A, Kaur N, Kaur H, Khamesipour F, Khan MN, Khan suheb MZ, Khanal V, Khatab K, Khatib MN, Kim G, Kim K, Kitila ATT, Komaki S, Krishan K, Krumkamp R, Kuddus MA, Kurniasari MD, Lahariya C, Latifinaibin K, Le NHH, Le TTT, Le TDT, Lee SW, LEPAPE A, Lerango TL, Li MC, Mahboobipour AA, Malhotra K, Mallhi TH, Manoharan A, Martinez-Guerra BA, Mathioudakis AG, Mattiello R, May J, McManigal B, McPhail SM, Mekene Meto T, Mendez-Lopez MAM, Meo SA, Merati M, Mestrovic T, Mhlanga L, Minh LHN, Misganaw A, Mishra V, Misra AK, Mohamed NS, Mohammadi E, Mohammed M, Mohammed M, Mokdad AH, Monasta L, Moore CE, Motappa R, Mougin V, Mousavi P, Mulita F, Mulu AA, Naghavi P, Naik GR, Nainu F, Nair TS, Nargus S, Negaresh M, Nguyen HTH, Nguyen DH, Nguyen VT, Nikolouzakis TK, Noman EA, Nri-Ezedi CA, Odetokun IA, Okwute PG, Olana MD, Olanipekun TO, Olasupo OO, Olivas-Martinez A, Ordak M, Ortiz-Brizuela E, Ouyahia A, Padubidri JR, Pak A, Pandey A, Pantazopoulos I, Parija PP, Parikh RR, Park S, Parthasarathi A, Pashaei A, Peprah P, Pham HT, Poddighe D, Pollard A, Ponce-De-Leon A, Prakash PY, Prates EJS, Quan NK, Raee P, Rahim F, Rahman M, Rahmati M, Ramasamy SK, Ranjan S, Rao IR, Rashid AM, Rattanavong S, Ravikumar N, Reddy MMRK, Redwan EMM, Reiner RC, Reyes LF, Roberts T, Rodrigues M, Rosenthal VD, Roy P, Runghien T, Saeed U, Saghazadeh A, Saheb Sharif-Askari N, Saheb Sharif-Askari F, Sahoo SS, Sahu M, Sakshaug JW, Salami AA, Saleh MA, Salehi omran H, Sallam M, Samadzadeh S, Samodra YL, Sanjeev RK, Sarasmita MA, Saravanan A, Sartorius B, Saulam J, Schumacher AE, Seyedi SA, Shafie M, Shahid S, Sham S, Shamim MA, Shamshirgaran MA, Shastry RP, Sherchan SP, Shiferaw D, Shittu A, Siddig EE, Sinto R, Sood A, Sorensen RJD, Stergachis A, Stoeva TZ, Swain CK, Szarpak L, Tamuzi JL, Temsah MH, Tessema MBT, Thangaraju P, Tran NM, Tran NH, Tumurkhuu M, Ty SS, Udoakang AJ, Ulhaq I, Umar TP, Umer AA, Vahabi SM, Vaithinathan AG, Van den Eynde J, Walson JL, Waqas M, Xing Y, Yadav MK, Yahya G, Yon DK, Zahedi Bialvaei A, Zakham F, Zeleke AM, Zhai C, Zhang Z, Zhang H, Zielińska M, Zheng P, Aravkin AY, Vos T, Hay SI, Mosser JF, Lim SS, Naghavi M, Murray CJL, Kyu HH. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Infect Dis 2024:S1473-3099(24)00176-2. [PMID: 38636536 DOI: 10.1016/s1473-3099(24)00176-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/19/2024] [Accepted: 03/07/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Lower respiratory infections (LRIs) are a major global contributor to morbidity and mortality. In 2020-21, non-pharmaceutical interventions associated with the COVID-19 pandemic reduced not only the transmission of SARS-CoV-2, but also the transmission of other LRI pathogens. Tracking LRI incidence and mortality, as well as the pathogens responsible, can guide health-system responses and funding priorities to reduce future burden. We present estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 of the burden of non-COVID-19 LRIs and corresponding aetiologies from 1990 to 2021, inclusive of pandemic effects on the incidence and mortality of select respiratory viruses, globally, regionally, and for 204 countries and territories. METHODS We estimated mortality, incidence, and aetiology attribution for LRI, defined by the GBD as pneumonia or bronchiolitis, not inclusive of COVID-19. We analysed 26 259 site-years of mortality data using the Cause of Death Ensemble model to estimate LRI mortality rates. We analysed all available age-specific and sex-specific data sources, including published literature identified by a systematic review, as well as household surveys, hospital admissions, health insurance claims, and LRI mortality estimates, to generate internally consistent estimates of incidence and prevalence using DisMod-MR 2.1. For aetiology estimation, we analysed multiple causes of death, vital registration, hospital discharge, microbial laboratory, and literature data using a network analysis model to produce the proportion of LRI deaths and episodes attributable to the following pathogens: Acinetobacter baumannii, Chlamydia spp, Enterobacter spp, Escherichia coli, fungi, group B streptococcus, Haemophilus influenzae, influenza viruses, Klebsiella pneumoniae, Legionella spp, Mycoplasma spp, polymicrobial infections, Pseudomonas aeruginosa, respiratory syncytial virus (RSV), Staphylococcus aureus, Streptococcus pneumoniae, and other viruses (ie, the aggregate of all viruses studied except influenza and RSV), as well as a residual category of other bacterial pathogens. FINDINGS Globally, in 2021, we estimated 344 million (95% uncertainty interval [UI] 325-364) incident episodes of LRI, or 4350 episodes (4120-4610) per 100 000 population, and 2·18 million deaths (1·98-2·36), or 27·7 deaths (25·1-29·9) per 100 000. 502 000 deaths (406 000-611 000) were in children younger than 5 years, among which 254 000 deaths (197 000-320 000) occurred in countries with a low Socio-demographic Index. Of the 18 modelled pathogen categories in 2021, S pneumoniae was responsible for the highest proportions of LRI episodes and deaths, with an estimated 97·9 million (92·1-104·0) episodes and 505 000 deaths (454 000-555 000) globally. The pathogens responsible for the second and third highest episode counts globally were other viral aetiologies (46·4 million [43·6-49·3] episodes) and Mycoplasma spp (25·3 million [23·5-27·2]), while those responsible for the second and third highest death counts were S aureus (424 000 [380 000-459 000]) and K pneumoniae (176 000 [158 000-194 000]). From 1990 to 2019, the global all-age non-COVID-19 LRI mortality rate declined by 41·7% (35·9-46·9), from 56·5 deaths (51·3-61·9) to 32·9 deaths (29·9-35·4) per 100 000. From 2019 to 2021, during the COVID-19 pandemic and implementation of associated non-pharmaceutical interventions, we estimated a 16·0% (13·1-18·6) decline in the global all-age non-COVID-19 LRI mortality rate, largely accounted for by a 71·8% (63·8-78·9) decline in the number of influenza deaths and a 66·7% (56·6-75·3) decline in the number of RSV deaths. INTERPRETATION Substantial progress has been made in reducing LRI mortality, but the burden remains high, especially in low-income and middle-income countries. During the COVID-19 pandemic, with its associated non-pharmaceutical interventions, global incident LRI cases and mortality attributable to influenza and RSV declined substantially. Expanding access to health-care services and vaccines, including S pneumoniae, H influenzae type B, and novel RSV vaccines, along with new low-cost interventions against S aureus, could mitigate the LRI burden and prevent transmission of LRI-causing pathogens. FUNDING Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care (UK).
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Yehualashet SS, Yesuf SA, Yezli S, Yiğit A, Yiğit V, Yigzaw ZA, Yismaw Y, Yon DK, Yonemoto N, Younis MZ, Yu C, Yu Y, Yusuf H, Zahid MH, Zakham F, Zaki L, Zaki N, Zaman BA, Zamora N, Zand R, Zandieh GGZ, Zar HJ, Zarrintan A, Zastrozhin MS, Zhang H, Zhang N, Zhang Y, Zhao H, Zhong C, Zhong P, Zhou J, Zhu Z, Ziafati M, Zielińska M, Zimsen SRM, Zoladl M, Zumla A, Zyoud SH, Vos T, Murray CJL. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00757-8. [PMID: 38642570 DOI: 10.1016/s0140-6736(24)00757-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/07/2024] [Accepted: 04/12/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. METHODS The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic. FINDINGS Global DALYs increased from 2·63 billion (95% UI 2·44-2·85) in 2010 to 2·88 billion (2·64-3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7-17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8-6·3) in 2020 and 7·2% (4·7-10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0-234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7-198·3]), neonatal disorders (186·3 million [162·3-214·9]), and stroke (160·4 million [148·0-171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3-51·7) and for diarrhoeal diseases decreased by 47·0% (39·9-52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54-1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5-9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0-19·8]), depressive disorders (16·4% [11·9-21·3]), and diabetes (14·0% [10·0-17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7-27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6-63·6) in 2010 to 62·2 years (59·4-64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6-2·9) between 2019 and 2021. INTERPRETATION Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades. FUNDING Bill & Melinda Gates Foundation.
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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00367-2. [PMID: 38582094 DOI: 10.1016/s0140-6736(24)00367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/15/2024] [Accepted: 02/22/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation.
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Bourne RRA, Jonas JB, Friedman D, Nangia V, Bron A, Tapply I, Fernandes AG, Cicinelli MV, Arrigo A, Leveziel N, Resnikoff S, Taylor HR, Sedighi T, Bikbov MM, Braithwaite T, Cheng CY, Congdon N, Del Monte MA, Ehrlich JR, Fricke T, Furtado JM, Gazzard G, George R, Hartnett ME, Kahloun R, Kempen JH, Khairallah M, Khanna RC, Kim JE, Lansingh VC, Leasher J, Naidoo KS, Nowak M, Pesudovs K, Peto T, Ramulu P, Topouzis F, Tsilimbaris M, Wang YX, Wang N, Flaxman S, Bourne RRA, Jonas JB, Casson RJ, Friedman DS, Nangia V, Bron AM, Tapply I, Fernandes AG, Cicinelli MV, Leveziel N, Briant PS, Vos T, Resnikoff S, Abate YH, Abate MD, Dolatabadi ZA, Abdollahi M, Aboagye RG, Abu-Gharbieh E, Aburuz S, Adnani QES, Aghamiri S, Ahinkorah BO, Ahmad D, Ahmadieh H, Ahmadzadeh H, Ahmed A, Alfaar AS, Alinia C, Almidani L, Amu H, Androudi S, Anil A, Arabloo J, Areda D, Ashraf T, Bagherieh S, Baltatu OC, Baran MF, Barrow A, Bashiri A, Bayileyegn NS, Bazvand F, Berhie AY, Bhatti JS, Bikbov M, Birck MG, Bitra VR, Bozic MM, Braithwaite T, Burkart K, Bustanji Y, Butt ZA, Cenderadewi M, Chattu VK, Coberly K, Dadras O, Dai X, Dascalu AM, Dastiridou A, Devanbu VGC, Dhimal M, Diaz D, Do THP, Do TC, Dziedzic AM, Ehrlich JR, Ekholuenetale M, Elhadi M, Emamian MH, Emamverdi M, Farrokhpour H, Fetensa G, Fischer F, Forouhari A, Fowobaje KR, Furtado JM, Gandhi AP, Gebregergis MWW, Goulart BNG, Gudeta MD, Gupta S, Gupta VK, Gupta VB, Heidari G, Hong SH, Huynh HH, Ibitoye SE, Ilic IM, Immurana M, Jayapal SK, Joseph N, Joshua CE, Kahloun R, Kandel H, Karaye IM, Kasraei H, Kebebew GM, Kempen JH, KhalafAlla MT, Khanal S, Khatib MN, Krishan K, Lahariya C, Leasher JL, Lim SS, Marzo RR, Maugeri A, Meng Y, Mestrovic T, Mishra M, Mohamed NS, Mojiri-forushani H, Mokdad AH, Momeni-Moghaddam H, Montazeri F, Mulita A, Murray CJL, Foodani MN, Naik GR, Natto ZS, Nayak BP, Negaresh M, Negash H, Nguyen DH, Oancea B, Olagunju AT, Olatubi MI, Osman WMS, Osuagwu UL, Padubidri JR, Panda-Jonas S, Pardhan S, Park S, Patel J, Perianayagam A, Pesudovs K, Pham HT, Prates EJS, Qattea I, Rahim F, Rahman M, Rapaka D, Rawaf S, Rezaei N, Roy P, Saddik B, Saeed U, Safi SZ, Safi S, Sakshaug JW, Saleh MA, Samuel VP, Samy AM, Saravanan A, Seylani A, Shaikh MA, Shamim MA, Shannawaz M, Shashamo BB, Shayan M, Shittu A, Siddig EE, Singh JA, Solomon Y, Sousa RARC, Tabatabaei SM, Tabish M, Ticoalu JHV, Toma TM, Tsatsakis A, Tsegay GM, Valizadeh R, Viskadourou M, Wassie GT, Wickramasinghe ND, Yon DK, You Y, Flaxman S, Steinmetz JD. Global estimates on the number of people blind or visually impaired by glaucoma: A meta-analysis from 2000 to 2020. Eye (Lond) 2024:10.1038/s41433-024-02995-5. [PMID: 38565601 DOI: 10.1038/s41433-024-02995-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 11/20/2023] [Accepted: 02/13/2024] [Indexed: 04/04/2024] Open
Abstract
OBJECTIVES To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by glaucoma and their proportion of the total number of vision-impaired individuals. METHODS A systematic review and meta-analysis of published population studies and grey literature from 2000 to 2020 was carried out to estimate global and regional trends in number of people with vision loss due to glaucoma. Moderate or severe vision loss (MSVI) was defined as visual acuity of 6/60 or better but <6/18 (moderate) and visual acuity of 3/60 or better but <6/60 (severe vision loss). Blindness was defined as presenting visual acuity <3/60. RESULTS Globally, in 2020, 3.61 million people were blind and nearly 4.14 million were visually impaired by glaucoma. Glaucoma accounted for 8.39% (95% uncertainty intervals [UIs]: 6.54, 10.29) of all blindness and 1.41% (95% UI: 1.10, 1.75) of all MSVI. Regionally, the highest proportion of blindness relating to glaucoma was found in high-income countries (26.12% [95% UI: 20.72, 32.09]), while the region with the highest age-standardized prevalence of glaucoma-related blindness and MSVI was Sub-Saharan Africa. Between 2000 and 2020, global age-standardized prevalence of glaucoma-related blindness among adults ≥50 years decreased by 26.06% among males (95% UI: 25.87, 26.24), and by 21.75% among females (95% UI: 21.54, 21.96), while MSVI due to glaucoma increased by 3.7% among males (95% UI: 3.42, 3.98), and by 7.3% in females (95% UI: 7.01, 7.59). CONCLUSIONS Within the last two decades, glaucoma has remained a major cause of blindness globally and regionally.
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Steinmetz JD, Seeher KM, Schiess N, Nichols E, Cao B, Servili C, Cavallera V, Cousin E, Hagins H, Moberg ME, Mehlman ML, Abate YH, Abbas J, Abbasi MA, Abbasian M, Abbastabar H, Abdelmasseh M, Abdollahi M, Abdollahi M, Abdollahifar MA, Abd-Rabu R, Abdulah DM, Abdullahi A, Abedi A, Abedi V, Abeldaño Zuñiga RA, Abidi H, Abiodun O, Aboagye RG, Abolhassani H, Aboyans V, Abrha WA, Abualhasan A, Abu-Gharbieh E, Aburuz S, Adamu LH, Addo IY, Adebayo OM, Adekanmbi V, Adekiya TA, Adikusuma W, Adnani QES, Adra S, Afework T, Afolabi AA, Afraz A, Afzal S, Aghamiri S, Agodi A, Agyemang-Duah W, Ahinkorah BO, Ahmad A, Ahmad D, Ahmad S, Ahmadzade AM, Ahmed A, Ahmed A, Ahmed H, Ahmed JQ, Ahmed LA, Ahmed MB, Ahmed SA, Ajami M, Aji B, Ajumobi O, Akade SE, Akbari M, Akbarialiabad H, Akhlaghi S, Akinosoglou K, Akinyemi RO, Akonde M, Al Hasan SM, Alahdab F, AL-Ahdal TMA, Al-amer RM, Albashtawy M, AlBataineh MT, Aldawsari KA, Alemi H, Alemi S, Algammal AM, Al-Gheethi AAS, Alhalaiqa FAN, Alhassan RK, Ali A, Ali EA, Ali L, Ali MU, Ali MM, Ali R, Ali S, Ali SSS, Ali Z, Alif SM, Alimohamadi Y, Aliyi AA, Aljofan M, Aljunid SM, Alladi S, Almazan JU, Almustanyir S, Al-Omari B, Alqahtani JS, Alqasmi I, Alqutaibi AY, Al-Shahi Salman R, Altaany Z, Al-Tawfiq JA, Altirkawi KA, Alvis-Guzman N, Al-Worafi YM, Aly H, Aly S, Alzoubi KH, Amani R, Amindarolzarbi A, Amiri S, Amirzade-Iranaq MH, Amu H, Amugsi DA, Amusa GA, Amzat J, Ancuceanu R, Anderlini D, Anderson DB, Andrei CL, Androudi S, Angappan D, Angesom TW, Anil A, Ansari-Moghaddam A, Anwer R, Arafat M, Aravkin AY, Areda D, Ariffin H, Arifin H, Arkew M, Ärnlöv J, Arooj M, Artamonov AA, Artanti KD, Aruleba RT, Asadi-Pooya AA, Asena TF, Asghari-Jafarabadi M, Ashraf M, Ashraf T, Atalell KA, Athari SS, Atinafu BTT, Atorkey P, Atout MMW, Atreya A, Aujayeb A, Avan A, Ayala Quintanilla BP, Ayatollahi H, Ayinde OO, Ayyoubzadeh SM, Azadnajafabad S, Azizi Z, Azizian K, Azzam AY, Babaei M, Badar M, Badiye AD, Baghdadi S, Bagherieh S, Bai R, Baig AA, Balakrishnan S, Balalla S, Baltatu OC, Banach M, Bandyopadhyay S, Banerjee I, Baran MF, Barboza MA, Barchitta M, Bardhan M, Barker-Collo SL, Bärnighausen TW, Barrow A, Bashash D, Bashiri H, Bashiru HA, Basiru A, Basso JD, Basu S, Batiha AMM, Batra K, Baune BT, Bedi N, Begde A, Begum T, Behnam B, Behnoush AH, Beiranvand M, Béjot Y, Bekele A, Belete MA, Belgaumi UI, Bemanalizadeh M, Bender RG, Benfor B, Bennett DA, Bensenor IM, Berice B, Bettencourt PJG, Beyene KA, Bhadra A, Bhagat DS, Bhangdia K, Bhardwaj N, Bhardwaj P, Bhargava A, Bhaskar S, Bhat AN, Bhat V, Bhatti GK, Bhatti JS, Bhatti R, Bijani A, Bikbov B, Bilalaga MM, Biswas A, Bitaraf S, Bitra VR, Bjørge T, Bodolica V, Bodunrin AO, Boloor A, Braithwaite D, Brayne C, Brenner H, Briko A, Bringas Vega ML, Brown J, Budke CM, Buonsenso D, Burkart K, Burns RA, Bustanji Y, Butt MH, Butt NS, Butt ZA, Cabral LS, Caetano dos Santos FL, Calina D, Campos-Nonato IR, Cao C, Carabin H, Cárdenas R, Carreras G, Carvalho AF, Castañeda-Orjuela CA, Casulli A, Catalá-López F, Catapano AL, Caye A, Cegolon L, Cenderadewi M, Cerin E, Chacón-Uscamaita PRU, Chan JSK, Chanie GS, Charan J, Chattu VK, Chekol Abebe E, Chen H, Chen J, Chi G, Chichagi F, Chidambaram SB, Chimoriya R, Ching PR, Chitheer A, Chong YY, Chopra H, Choudhari SG, Chowdhury EK, Chowdhury R, Christensen H, Chu DT, Chukwu IS, Chung E, Coberly K, Columbus A, Comachio J, Conde J, Cortesi PA, Costa VM, Couto RAS, Criqui MH, Cruz-Martins N, Dabbagh Ohadi MA, Dadana S, Dadras O, Dai X, Dai Z, D'Amico E, Danawi HA, Dandona L, Dandona R, Darwish AH, Das S, Das S, Dascalu AM, Dash NR, Dashti M, De la Hoz FP, de la Torre-Luque A, De Leo D, Dean FE, Dehghan A, Dehghan A, Dejene H, Demant D, Demetriades AK, Demissie S, Deng X, Desai HD, Devanbu VGC, Dhama K, Dharmaratne SD, Dhimal M, Dias da Silva D, Diaz D, Dibas M, Ding DD, Dinu M, Dirac MA, Diress M, Do TC, Do THP, Doan KDK, Dodangeh M, Doheim MF, Dokova KG, Dongarwar D, Dsouza HL, Dube J, Duraisamy S, Durojaiye OC, Dutta S, Dziedzic AM, Edinur HA, Eissazade N, Ekholuenetale M, Ekundayo TC, El Nahas N, El Sayed I, Elahi Najafi MA, Elbarazi I, Elemam NM, Elgar FJ, Elgendy IY, Elhabashy HR, Elhadi M, Elilo LT, Ellenbogen RG, Elmeligy OAA, Elmonem MA, Elshaer M, Elsohaby I, Emamverdi M, Emeto TI, Endres M, Esezobor CI, Eskandarieh S, Fadaei A, Fagbamigbe AF, Fahim A, Faramarzi A, Fares J, Farjoud Kouhanjani M, Faro A, Farzadfar F, Fatehizadeh A, Fathi M, Fathi S, Fatima SAF, Feizkhah A, Fereshtehnejad SM, Ferrari AJ, Ferreira N, Fetensa G, Firouraghi N, Fischer F, Fonseca AC, Force LM, Fornari A, Foroutan B, Fukumoto T, Gadanya MA, Gaidhane AM, Galali Y, Galehdar N, Gan Q, Gandhi AP, Ganesan B, Gardner WM, Garg N, Gau SY, Gautam RK, Gebre T, Gebrehiwot M, Gebremeskel GG, Gebreslassie HG, Getacher L, Ghaderi Yazdi B, Ghadirian F, Ghaffarpasand F, Ghanbari R, Ghasemi M, Ghazy RM, Ghimire S, Gholami A, Gholamrezanezhad A, Ghotbi E, Ghozy S, Gialluisi A, Gill PS, Glasstetter LM, Gnedovskaya EV, Golchin A, Golechha M, Goleij P, Golinelli D, Gomes-Neto M, Goulart AC, Goyal A, Gray RJ, Grivna M, Guadie HA, Guan B, Guarducci G, Guicciardi S, Gunawardane DA, Guo H, Gupta B, Gupta R, Gupta S, Gupta VB, Gupta VK, Gutiérrez RA, Habibzadeh F, Hachinski V, Haddadi R, Hadei M, Hadi NR, Haep N, Haile TG, Haj-Mirzaian A, Hall BJ, Halwani R, Hameed S, Hamiduzzaman M, Hammoud A, Han H, Hanifi N, Hankey GJ, Hannan MA, Hao J, Harapan H, Hareru HE, Hargono A, Harlianto NI, Haro JM, Hartman NN, Hasaballah AI, Hasan F, Hasani H, Hasanian M, Hassan A, Hassan S, Hassanipour S, Hassankhani H, Hassen MB, Haubold J, Hay SI, Hayat K, Hegazy MI, Heidari G, Heidari M, Heidari-Soureshjani R, Hesami H, Hezam K, Hiraike Y, Hoffman HJ, Holla R, Hopf KP, Horita N, Hossain MM, Hossain MB, Hossain S, Hosseinzadeh H, Hosseinzadeh M, Hostiuc S, Hu C, Huang J, Huda MN, Hussain J, Hussein NR, Huynh HH, Hwang BF, Ibitoye SE, Ilaghi M, Ilesanmi OS, Ilic IM, Ilic MD, Immurana M, Iravanpour F, Islam SMS, Ismail F, Iso H, Isola G, Iwagami M, Iwu CCD, Iyer M, Jaan A, Jacob L, Jadidi-Niaragh F, Jafari M, Jafarinia M, Jafarzadeh A, Jahankhani K, Jahanmehr N, Jahrami H, Jaiswal A, Jakovljevic M, Jamora RDG, Jana S, Javadi N, Javed S, Javeed S, Jayapal SK, Jayaram S, Jiang H, Johnson CO, Johnson WD, Jokar M, Jonas JB, Joseph A, Joseph N, Joshua CE, Jürisson M, Kabir A, Kabir Z, Kabito GG, Kadashetti V, Kafi F, Kalani R, Kalantar F, Kaliyadan F, Kamath A, Kamath S, Kanchan T, Kandel A, Kandel H, Kanmodi KK, Karajizadeh M, Karami J, Karanth SD, Karaye IM, Karch A, Karimi A, Karimi H, Karimi Behnagh A, Kasraei H, Kassebaum NJ, Kauppila JH, Kaur H, Kaur N, Kayode GA, Kazemi F, Keikavoosi-Arani L, Keller C, Keykhaei M, Khadembashiri MA, Khader YS, Khafaie MA, Khajuria H, Khalaji A, Khamesipour F, Khammarnia M, Khan M, Khan MAB, Khan YH, Khan Suheb MZ, Khanmohammadi S, Khanna T, Khatab K, Khatatbeh H, Khatatbeh MM, Khateri S, Khatib MN, Khayat Kashani HR, Khonji MS, khorashadizadeh F, Khormali M, Khubchandani J, Kian S, Kim G, Kim J, Kim MS, Kim YJ, Kimokoti RW, Kisa A, Kisa S, Kivimäki M, Kochhar S, Kolahi AA, Koly KN, Kompani F, Koroshetz WJ, Kosen S, Kourosh Arami M, Koyanagi A, Kravchenko MA, Krishan K, Krishnamoorthy V, Kuate Defo B, Kuddus MA, Kumar A, Kumar GA, Kumar M, Kumar N, Kumsa NB, Kundu S, Kurniasari MD, Kusuma D, Kuttikkattu A, Kyu HH, La Vecchia C, Ladan MA, Lahariya C, Laksono T, Lal DK, Lallukka T, Lám J, Lami FH, Landires I, Langguth B, Lasrado S, Latief K, Latifinaibin K, Lau KMM, Laurens MB, Lawal BK, Le LKD, Le TTT, Ledda C, Lee M, Lee SW, Lee SW, Lee WC, Lee YH, Leonardi M, Lerango TL, Li MC, Li W, Ligade VS, Lim SS, Linehan C, Liu C, Liu J, Liu W, Lo CH, Lo WD, Lobo SW, Logroscino G, Lopes G, Lopukhov PD, Lorenzovici L, Lorkowski S, Loureiro JA, Lubinda J, Lucchetti G, Lutzky Saute R, Ma ZF, Mabrok M, Machoy M, Madadizadeh F, Magdy Abd El Razek M, Maghazachi AA, Maghbouli N, Mahjoub S, Mahmoudi M, Majeed A, Malagón-Rojas JN, Malakan Rad E, Malhotra K, Malik AA, Malik I, Mallhi TH, Malta DC, Manilal A, Mansouri V, Mansournia MA, Marasini BP, Marateb HR, Maroufi SF, Martinez-Raga J, Martini S, Martins-Melo FR, Martorell M, März W, Marzo RR, Massano J, Mathangasinghe Y, Mathews E, Maude RJ, Maugeri A, Maulik PK, Mayeli M, Mazaheri M, McAlinden C, McGrath JJ, Meena JK, Mehndiratta MM, Mendez-Lopez MAM, Mendoza W, Mendoza-Cano O, Menezes RG, Merati M, Meretoja A, Merkin A, Mersha AM, Mestrovic T, Mi T, Miazgowski T, Michalek IM, Mihretie ET, Minh LHN, Mirfakhraie R, Mirica A, Mirrakhimov EM, Mirzaei M, Misganaw A, Misra S, Mithra P, Mizana BA, Mohamadkhani A, Mohamed NS, Mohammadi E, Mohammadi H, Mohammadi S, Mohammadi S, Mohammadshahi M, Mohammed M, Mohammed S, Mohammed S, Mohan S, Mojiri-forushani H, Moka N, Mokdad AH, Molinaro S, Möller H, Monasta L, Moniruzzaman M, Montazeri F, Moradi M, Moradi Y, Moradi-Lakeh M, Moraga P, Morovatdar N, Morrison SD, Mosapour A, Mosser JF, Mossialos E, Motaghinejad M, Mousavi P, Mousavi SE, Mubarik S, Muccioli L, Mughal F, Mukoro GD, Mulita A, Mulita F, Musaigwa F, Mustafa A, Mustafa G, Muthu S, Nagarajan AJ, Naghavi P, Naik GR, Nainu F, Nair TS, Najmuldeen HHR, Nakhostin Ansari N, Nambi G, Namdar Areshtanab H, Nargus S, Nascimento BR, Naser AY, Nashwan AJJ, Nasoori H, Nasreldein A, Natto ZS, Nauman J, Nayak BP, Nazri-Panjaki A, Negaresh M, Negash H, Negoi I, Negoi RI, Negru SM, Nejadghaderi SA, Nematollahi MH, Nesbit OD, Newton CRJ, Nguyen DH, Nguyen HTH, Nguyen HQ, Nguyen NTT, Nguyen PT, Nguyen VT, Niazi RK, Nikolouzakis TK, Niranjan V, Nnyanzi LA, Noman EA, Noroozi N, Norrving B, Noubiap JJ, Nri-Ezedi CA, Ntaios G, Nuñez-Samudio V, Nurrika D, Oancea B, Odetokun IA, O'Donnell MJ, Ogunsakin RE, Oguta JO, Oh IH, Okati-Aliabad H, Okeke SR, Okekunle AP, Okonji OC, Okwute PG, Olagunju AT, Olaiya MT, Olana MD, Olatubi MI, Oliveira GMM, Olufadewa II, Olusanya BO, Omar Bali A, Ong S, Onwujekwe OE, Ordak M, Orji AU, Ortega-Altamirano DV, Osuagwu UL, Otstavnov N, Otstavnov SS, Ouyahia A, Owolabi MO, P A MP, Pacheco-Barrios K, Padubidri JR, Pal PK, Palange PN, Palladino C, Palladino R, Palma-Alvarez RF, Pan F, Panagiotakos D, Panda-Jonas S, Pandey A, Pandey A, Pandian JD, Pangaribuan HU, Pantazopoulos I, Pardhan S, Parija PP, Parikh RR, Park S, Parthasarathi A, Pashaei A, Patel J, Patil S, Patoulias D, Pawar S, Pedersini P, Pensato U, Pereira DM, Pereira J, Pereira MO, Peres MFP, Perico N, Perna S, Petcu IR, Petermann-Rocha FE, Pham HT, Phillips MR, Pinilla-Monsalve GD, Piradov MA, Plotnikov E, Poddighe D, Polat B, Poluru R, Pond CD, Poudel GR, Pouramini A, Pourbagher-Shahri AM, Pourfridoni M, Pourtaheri N, Prakash PY, Prakash S, Prakash V, Prates EJS, Pritchett N, Purnobasuki H, Qasim NH, Qattea I, Qian G, Radhakrishnan V, Raee P, Raeisi Shahraki H, Rafique I, Raggi A, Raghav PR, Rahati MM, Rahim F, Rahimi Z, Rahimifard M, Rahman MO, Rahman MHU, Rahman M, Rahman MA, Rahmani AM, Rahmani S, Rahmani Youshanlouei H, Rahmati M, Raj Moolambally S, Rajabpour-Sanati A, Ramadan H, Ramasamy SK, Ramasubramani P, Ramazanu S, Rancic N, Rao IR, Rao SJ, Rapaka D, Rashedi V, Rashid AM, Rashidi MM, Rashidi Alavijeh M, Rasouli-Saravani A, Rawaf S, Razo C, Redwan EMM, Rekabi Bana A, Remuzzi G, Rezaei N, Rezaei N, Rezaei N, Rezaeian M, Rhee TG, Riad A, Robinson SR, Rodrigues M, Rodriguez JAB, Roever L, Rogowski ELB, Romoli M, Ronfani L, Roy P, Roy Pramanik K, Rubagotti E, Ruiz MA, Russ TC, S Sunnerhagen K, Saad AMA, Saadatian Z, Saber K, SaberiKamarposhti M, Sacco S, Saddik B, Sadeghi E, Sadeghian S, Saeed U, Saeed U, Safdarian M, Safi SZ, Sagar R, Sagoe D, Saheb Sharif-Askari F, Saheb Sharif-Askari N, Sahebkar A, Sahoo SS, Sahraian MA, Sajedi SA, Sakshaug JW, Saleh MA, Salehi Omran H, Salem MR, Salimi S, Samadi Kafil H, Samadzadeh S, Samargandy S, Samodra YL, Samuel VP, Samy AM, Sanadgol N, Sanjeev RK, Sanmarchi F, Santomauro DF, Santri IN, Santric-Milicevic MM, Saravanan A, Sarveazad A, Satpathy M, Saylan M, Sayyah M, Scarmeas N, Schlaich MP, Schuermans A, Schwarzinger M, Schwebel DC, Selvaraj S, Sendekie AK, Sengupta P, Senthilkumaran S, Serban D, Sergindo MT, Sethi Y, SeyedAlinaghi S, Seylani A, Shabani M, Shabany M, Shafie M, Shahabi S, Shahbandi A, Shahid S, Shahraki-Sanavi F, Shahsavari HR, Shahwan MJ, Shaikh MA, Shaji KS, Sham S, Shama ATT, Shamim MA, Shams-Beyranvand M, Shamsi MA, Shanawaz M, Sharath M, Sharfaei S, Sharifan A, Sharma M, Sharma R, Shashamo BB, Shayan M, Sheikhi RA, Shekhar S, Shen J, Shenoy SM, Shetty PH, Shiferaw DS, Shigematsu M, Shiri R, Shittu A, Shivakumar KM, Shokri F, Shool S, Shorofi SA, Shrestha S, Siankam Tankwanchi AB, Siddig EE, Sigfusdottir ID, Silva JP, Silva LMLR, Sinaei E, Singh BB, Singh G, Singh P, Singh S, Sirota SB, Sivakumar S, Sohag AAM, Solanki R, Soleimani H, Solikhah S, Solomon Y, Solomon Y, Song S, Song Y, Sotoudeh H, Spartalis M, Stark BA, Starnes JR, Starodubova AV, Stein DJ, Steiner TJ, Stovner LJ, Suleman M, Suliankatchi Abdulkader R, Sultana A, Sun J, Sunkersing D, Sunny A, Susianti H, Swain CK, Szeto MD, Tabarés-Seisdedos R, Tabatabaei SM, Tabatabai S, Tabish M, Taheri M, Tahvildari A, Tajbakhsh A, Tampa M, Tamuzi JJLL, Tan KK, Tang H, Tareke M, Tarigan IU, Tat NY, Tat VY, Tavakoli Oliaee R, Tavangar SM, Tavasol A, Tefera YM, Tehrani-Banihashemi A, Temesgen WA, Temsah MH, Teramoto M, Tesfaye AH, Tesfaye EG, Tesler R, Thakali O, Thangaraju P, Thapa R, Thapar R, Thomas NK, Thrift AG, Ticoalu JHV, Tillawi T, Toghroli R, Tonelli M, Tovani-Palone MR, Traini E, Tran NM, Tran NH, Tran PV, Tromans SJ, Truelsen TC, Truyen TTTT, Tsatsakis A, Tsegay GM, Tsermpini EE, Tualeka AR, Tufa DG, Ubah CS, Udoakang AJ, Ulhaq I, Umair M, Umakanthan S, Umapathi KK, Unim B, Unnikrishnan B, Vaithinathan AG, Vakilian A, Valadan Tahbaz S, Valizadeh R, Van den Eynde J, Vart P, Varthya SB, Vasankari TJ, Vaziri S, Vellingiri B, Venketasubramanian N, Verras GI, Vervoort D, Villafañe JH, Villani L, Vinueza Veloz AF, Viskadourou M, Vladimirov SK, Vlassov V, Volovat SR, Vu LT, Vujcic IS, Wagaye B, Waheed Y, Wahood W, Walde MT, Wang F, Wang S, Wang Y, Wang YP, Waqas M, Waris A, Weerakoon KG, Weintraub RG, Weldemariam AH, Westerman R, Whisnant JL, Wickramasinghe DP, Wickramasinghe ND, Willekens B, Wilner LB, Winkler AS, Wolfe CDA, Wu AM, Wulf Hanson S, Xu S, Xu X, Yadollahpour A, Yaghoubi S, Yahya G, Yamagishi K, Yang L, Yano Y, Yao Y, Yehualashet SS, Yeshaneh A, Yesiltepe M, Yi S, Yiğit A, Yiğit V, Yon DK, Yonemoto N, You Y, Younis MZ, Yu C, Yusuf H, Zadey S, Zahedi M, Zakham F, Zaki N, Zali A, Zamagni G, Zand R, Zandieh GGZ, Zangiabadian M, Zarghami A, Zastrozhin MS, Zeariya MGM, Zegeye ZB, Zeukeng F, Zhai C, Zhang C, Zhang H, Zhang Y, Zhang ZJ, Zhao H, Zhao Y, Zheng P, Zhou H, Zhu B, Zhumagaliuly A, Zielińska M, Zikarg YT, Zoladl M, Murray CJL, Ong KL, Feigin VL, Vos T, Dua T. Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol 2024; 23:344-381. [PMID: 38493795 PMCID: PMC10949203 DOI: 10.1016/s1474-4422(24)00038-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021. METHODS We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined. FINDINGS Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378-521), affecting 3·40 billion (3·20-3·62) individuals (43·1%, 40·5-45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7-26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6-38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5-32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7-2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer. INTERPRETATION As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed. FUNDING Bill & Melinda Gates Foundation.
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Ledesma JR, Ma J, Zhang M, Basting AVL, Chu HT, Vongpradith A, Novotney A, LeGrand KE, Xu YY, Dai X, Nicholson SI, Stafford LK, Carter A, Ross JM, Abbastabar H, Abdoun M, Abdulah DM, Aboagye RG, Abolhassani H, Abrha WA, Abubaker Ali H, Abu-Gharbieh E, Aburuz S, Addo IY, Adepoju AV, Adhikari K, Adnani QES, Adra S, Afework A, Aghamiri S, Agyemang-Duah W, Ahinkorah BO, Ahmad D, Ahmad S, Ahmadzade AM, Ahmed H, Ahmed M, Ahmed A, Akinosoglou K, AL-Ahdal TMA, Alam N, Albashtawy M, AlBataineh MT, Al-Gheethi AAS, Ali A, Ali EA, Ali L, Ali Z, Ali SSS, Allel K, Altaf A, Al-Tawfiq JA, Alvis-Guzman N, Alvis-Zakzuk NJ, Amani R, Amusa GA, Amzat J, Andrews JR, Anil A, Anwer R, Aravkin AY, Areda D, Artamonov AA, Aruleba RT, Asemahagn MA, Atre SR, Aujayeb A, Azadi D, Azadnajafabad S, Azzam AY, Badar M, Badiye AD, Bagherieh S, Bahadorikhalili S, Baig AA, Banach M, Banik B, Bardhan M, Barqawi HJ, Basharat Z, Baskaran P, Basu S, Beiranvand M, Belete MA, Belew MA, Belgaumi UI, Beloukas A, Bettencourt PJG, Bhagavathula AS, Bhardwaj N, Bhardwaj P, Bhargava A, Bhat V, Bhatti JS, Bhatti GK, Bikbov B, Bitra VR, Bjegovic-Mikanovic V, Buonsenso D, Burkart K, Bustanji Y, Butt ZA, Camargos P, Cao Y, Carr S, Carvalho F, Cegolon L, Cenderadewi M, Cevik M, Chahine Y, Chattu VK, Ching PR, Chopra H, Chung E, Claassens MM, Coberly K, Cruz-Martins N, Dabo B, Dadana S, Dadras O, Darban I, Darega Gela J, Darwesh AM, Dashti M, Demessa BH, Demisse B, Demissie S, Derese AMA, Deribe K, Desai HD, Devanbu VGC, Dhali A, Dhama K, Dhingra S, Do THP, Dongarwar D, Dsouza HL, Dube J, Dziedzic AM, Ed-Dra A, Efendi F, Effendi DE, Eftekharimehrabad A, Ekadinata N, Ekundayo TC, Elhadi M, Elilo LT, Emeto TI, Engelbert Bain L, Fagbamigbe AF, Fahim A, Feizkhah A, Fetensa G, Fischer F, Gaipov A, Gandhi AP, Gautam RK, Gebregergis MW, Gebrehiwot M, Gebrekidan KG, Ghaffari K, Ghassemi F, Ghazy RM, Goodridge A, Goyal A, Guan SY, Gudeta MD, Guled RA, Gultom NB, Gupta VB, Gupta VK, Gupta S, Hagins H, Hailu SG, Hailu WB, Hamidi S, Hanif A, Harapan H, Hasan RS, Hassan S, Haubold J, Hezam K, Hong SH, Horita N, Hossain MB, Hosseinzadeh M, Hostiuc M, Hostiuc S, Huynh HH, Ibitoye SE, Ikuta KS, Ilic IM, Ilic MD, Islam MR, Ismail NE, Ismail F, Jafarzadeh A, Jakovljevic M, Jalili M, Janodia MD, Jomehzadeh N, Jonas JB, Joseph N, Joshua CE, Kabir Z, Kamble BD, Kanchan T, Kandel H, Kanmodi KK, Kantar RS, Karaye IM, Karimi Behnagh A, Kassa GG, Kaur RJ, Kaur N, Khajuria H, Khamesipour F, Khan YH, Khan MN, Khan Suheb MZ, Khatab K, Khatami F, Kim MS, Kosen S, Koul PA, Koulmane Laxminarayana SL, Krishan K, Kucuk Bicer B, Kuddus MA, Kulimbet M, Kumar N, Lal DK, Landires I, Latief K, Le TDT, Le TTT, Ledda C, Lee M, Lee SW, Lerango TL, Lim SS, Liu C, Liu X, Lopukhov PD, Luo H, Lv H, Mahajan PB, Mahboobipour AA, Majeed A, Malakan Rad E, Malhotra K, Malik MSA, Malinga LA, Mallhi TH, Manilal A, Martinez-Guerra BA, Martins-Melo FR, Marzo RR, Masoumi-Asl H, Mathur V, Maude RJ, Mehrotra R, Memish ZA, Mendoza W, Menezes RG, Merza MA, Mestrovic T, Mhlanga L, Misra S, Misra AK, Mithra P, Moazen B, Mohammed H, Mokdad AH, Monasta L, Moore CE, Mousavi P, Mulita F, Musaigwa F, Muthusamy R, Nagarajan AJ, Naghavi P, Naik GR, Naik G, Nair S, Nair TS, Natto ZS, Nayak BP, Negash H, Nguyen DH, Nguyen VT, Niazi RK, Nnaji CA, Nnyanzi LA, Noman EA, Nomura S, Oancea B, Obamiro KO, Odetokun IA, Odo DBO, Odukoya OO, Oh IH, Okereke CO, Okonji OC, Oren E, Ortiz-Brizuela E, Osuagwu UL, Ouyahia A, P A MP, Parija PP, Parikh RR, Park S, Parthasarathi A, Patil S, Pawar S, Peng M, Pepito VCF, Peprah P, Perdigão J, Perico N, Pham HT, Postma MJ, Prabhu ARA, Prasad M, Prashant A, Prates EJS, Rahim F, Rahman M, Rahman MA, Rahmati M, Rajaa S, Ramasamy SK, Rao IR, Rao SJ, Rapaka D, Rashid AM, Ratan ZA, Ravikumar N, Rawaf S, Reddy MMRK, Redwan EMM, Remuzzi G, Reyes LF, Rezaei N, Rezaeian M, Rezahosseini O, Rodrigues M, Roy P, Ruela GDA, Sabour S, Saddik B, Saeed U, Safi SZ, Saheb Sharif-Askari N, Saheb Sharif-Askari F, Sahebkar A, Sahiledengle B, Sahoo SS, Salam N, Salami AA, Saleem S, Saleh MA, Samadi Kafil H, Samadzadeh S, Samodra YL, Sanjeev RK, Saravanan A, Sawyer SM, Selvaraj S, Senapati S, Senthilkumaran S, Shah PA, Shahid S, Shaikh MA, Sham S, Shamshirgaran MA, Shanawaz M, Sharath M, Sherchan SP, Shetty RS, Shirzad-Aski H, Shittu A, Siddig EE, Silva JP, Singh S, Singh P, Singh H, Singh JA, Siraj MS, Siswanto S, Solanki R, Solomon Y, Soriano JB, Sreeramareddy CT, Srivastava VK, Steiropoulos P, Swain CK, Tabuchi T, Tampa M, Tamuzi JJLL, Tat NY, Tavakoli Oliaee R, Teklay G, Tesfaye EG, Tessema B, Thangaraju P, Thapar R, Thum CCC, Ticoalu JHV, Tleyjeh IM, Tobe-Gai R, Toma TM, Tram KH, Udoakang AJ, Umar TP, Umeokonkwo CD, Vahabi SM, Vaithinathan AG, van Boven JFM, Varthya SB, Wang Z, Warsame MSA, Westerman R, Wonde TE, Yaghoubi S, Yi S, Yiğit V, Yon DK, Yonemoto N, Yu C, Zakham F, Zangiabadian M, Zeukeng F, Zhang H, Zhao Y, Zheng P, Zielińska M, Salomon JA, Reiner Jr RC, Naghavi M, Vos T, Hay SI, Murray CJL, Kyu HH. Global, regional, and national age-specific progress towards the 2020 milestones of the WHO End TB Strategy: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Infect Dis 2024:S1473-3099(24)00007-0. [PMID: 38518787 DOI: 10.1016/s1473-3099(24)00007-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 12/09/2023] [Accepted: 01/08/2024] [Indexed: 03/24/2024]
Abstract
BACKGROUND Global evaluations of the progress towards the WHO End TB Strategy 2020 interim milestones on mortality (35% reduction) and incidence (20% reduction) have not been age specific. We aimed to assess global, regional, and national-level burdens of and trends in tuberculosis and its risk factors across five separate age groups, from 1990 to 2021, and to report on age-specific progress between 2015 and 2020. METHODS We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD 2021) analytical framework to compute age-specific tuberculosis mortality and incidence estimates for 204 countries and territories (1990-2021 inclusive). We quantified tuberculosis mortality among individuals without HIV co-infection using 22 603 site-years of vital registration data, 1718 site-years of verbal autopsy data, 825 site-years of sample-based vital registration data, 680 site-years of mortality surveillance data, and 9 site-years of minimally invasive tissue sample (MITS) diagnoses data as inputs into the Cause of Death Ensemble modelling platform. Age-specific HIV and tuberculosis deaths were established with a population attributable fraction approach. We analysed all available population-based data sources, including prevalence surveys, annual case notifications, tuberculin surveys, and tuberculosis mortality, in DisMod-MR 2.1 to produce internally consistent age-specific estimates of tuberculosis incidence, prevalence, and mortality. We also estimated age-specific tuberculosis mortality without HIV co-infection that is attributable to the independent and combined effects of three risk factors (smoking, alcohol use, and diabetes). As a secondary analysis, we examined the potential impact of the COVID-19 pandemic on tuberculosis mortality without HIV co-infection by comparing expected tuberculosis deaths, modelled with trends in tuberculosis deaths from 2015 to 2019 in vital registration data, with observed tuberculosis deaths in 2020 and 2021 for countries with available cause-specific mortality data. FINDINGS We estimated 9·40 million (95% uncertainty interval [UI] 8·36 to 10·5) tuberculosis incident cases and 1·35 million (1·23 to 1·52) deaths due to tuberculosis in 2021. At the global level, the all-age tuberculosis incidence rate declined by 6·26% (5·27 to 7·25) between 2015 and 2020 (the WHO End TB strategy evaluation period). 15 of 204 countries achieved a 20% decrease in all-age tuberculosis incidence between 2015 and 2020, eight of which were in western sub-Saharan Africa. When stratified by age, global tuberculosis incidence rates decreased by 16·5% (14·8 to 18·4) in children younger than 5 years, 16·2% (14·2 to 17·9) in those aged 5-14 years, 6·29% (5·05 to 7·70) in those aged 15-49 years, 5·72% (4·02 to 7·39) in those aged 50-69 years, and 8·48% (6·74 to 10·4) in those aged 70 years and older, from 2015 to 2020. Global tuberculosis deaths decreased by 11·9% (5·77 to 17·0) from 2015 to 2020. 17 countries attained a 35% reduction in deaths due to tuberculosis between 2015 and 2020, most of which were in eastern Europe (six countries) and central Europe (four countries). There was variable progress by age: a 35·3% (26·7 to 41·7) decrease in tuberculosis deaths in children younger than 5 years, a 29·5% (25·5 to 34·1) decrease in those aged 5-14 years, a 15·2% (10·0 to 20·2) decrease in those aged 15-49 years, a 7·97% (0·472 to 14·1) decrease in those aged 50-69 years, and a 3·29% (-5·56 to 9·07) decrease in those aged 70 years and older. Removing the combined effects of the three attributable risk factors would have reduced the number of all-age tuberculosis deaths from 1·39 million (1·28 to 1·54) to 1·00 million (0·703 to 1·23) in 2020, representing a 36·5% (21·5 to 54·8) reduction in tuberculosis deaths compared to those observed in 2015. 41 countries were included in our analysis of the impact of the COVID-19 pandemic on tuberculosis deaths without HIV co-infection in 2020, and 20 countries were included in the analysis for 2021. In 2020, 50 900 (95% CI 49 700 to 52 400) deaths were expected across all ages, compared to an observed 45 500 deaths, corresponding to 5340 (4070 to 6920) fewer deaths; in 2021, 39 600 (38 300 to 41 100) deaths were expected across all ages compared to an observed 39 000 deaths, corresponding to 657 (-713 to 2180) fewer deaths. INTERPRETATION Despite accelerated progress in reducing the global burden of tuberculosis in the past decade, the world did not attain the first interim milestones of the WHO End TB Strategy in 2020. The pace of decline has been unequal with respect to age, with older adults (ie, those aged >50 years) having the slowest progress. As countries refine their national tuberculosis programmes and recalibrate for achieving the 2035 targets, they could consider learning from the strategies of countries that achieved the 2020 milestones, as well as consider targeted interventions to improve outcomes in older age groups. FUNDING Bill & Melinda Gates Foundation.
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Schumacher AE, Kyu HH, Aali A, Abbafati C, Abbas J, Abbasgholizadeh R, Abbasi MA, Abbasian M, Abd ElHafeez S, Abdelmasseh M, Abd-Elsalam S, Abdelwahab A, Abdollahi M, Abdoun M, Abdullahi A, Abdurehman AM, Abebe M, Abedi A, Abedi A, Abegaz TM, Abeldaño Zuñiga RA, Abhilash ES, Abiodun OO, Aboagye RG, Abolhassani H, Abouzid M, Abreu LG, Abrha WA, Abrigo MRM, Abtahi D, Abu Rumeileh S, Abu-Rmeileh NME, Aburuz S, Abu-Zaid A, Acuna JM, Adair T, Addo IY, Adebayo OM, Adegboye OA, Adekanmbi V, Aden B, Adepoju AV, Adetunji CO, Adeyeoluwa TE, Adeyomoye OI, Adha R, Adibi A, Adikusuma W, Adnani QES, Adra S, Afework A, Afolabi AA, Afraz A, Afyouni S, Afzal S, Agasthi P, Aghamiri S, Agodi A, Agyemang-Duah W, Ahinkorah BO, Ahmad A, Ahmad D, Ahmad F, Ahmad MM, Ahmad T, Ahmadi K, Ahmadzade AM, Ahmadzade M, Ahmed A, Ahmed H, Ahmed LA, Ahmed MB, Ahmed SA, Ajami M, Aji B, Ajumobi O, Akalu GT, Akara EM, Akinosoglou K, Akkala S, Akyirem S, Al Hamad H, Al Hasan SM, Al Homsi A, Al Qadire M, Ala M, Aladelusi TO, AL-Ahdal TMA, Alalalmeh SO, Al-Aly Z, Alam K, Alam M, Alam Z, Al-amer RM, Alanezi FM, Alanzi TM, Albashtawy M, AlBataineh MT, Aldridge RW, Alemi S, Al-Eyadhy A, Al-Gheethi AAS, Alhabib KF, Alhalaiqa FAN, Al-Hanawi MK, Ali A, Ali A, Ali BA, Ali H, Ali MU, Ali R, Ali SSS, Ali Z, Alian Samakkhah S, Alicandro G, Alif SM, Aligol M, Alimi R, Aliyi AA, Al-Jumaily A, Aljunid SM, Almahmeed W, Al-Marwani S, Al-Maweri SAA, Almazan JU, Al-Mekhlafi HM, Almidani O, Alomari MA, Alonso N, Alqahtani JS, Alqutaibi AY, Al-Sabah SK, Altaf A, Al-Tawfiq JA, Altirkawi KA, Alvi FJ, Alwafi H, Al-Worafi YM, Aly H, Alzoubi KH, Amare AT, Ameyaw EK, Amhare AF, Amin TT, Amindarolzarbi A, Aminian Dehkordi J, Amiri S, Amu H, Amugsi DA, Amzat J, Ancuceanu R, Anderlini D, Andrade PP, Andrei CL, Andrei T, Angappan D, Anil A, Anjum A, Antony CM, Antriyandarti E, Anuoluwa IA, Anwar SL, Anyasodor AE, Appiah SCY, Aqeel M, Arabloo J, Arabzadeh Bahri R, Arab-Zozani M, Arafat M, Araújo AM, Aravkin AY, Aremu A, Ariffin H, Aripov T, Armocida B, Arooj M, Artamonov AA, Artanti KD, Arulappan J, Aruleba IT, Aruleba RT, Arumugam A, Asaad M, Asgary S, Ashemo MY, Ashraf M, Asika MO, Athari SS, Atout MMW, Atreya A, Attia S, Aujayeb A, Avan A, Awotidebe AW, Ayala Quintanilla BP, Ayanore MA, Ayele GM, Ayuso-Mateos JL, Ayyoubzadeh SM, Azadnajafabad S, Azhar GS, Aziz S, Azzam AY, Babashahi M, Babu AS, Badar M, Badawi A, Badiye AD, Baghdadi S, Bagheri N, Bagherieh S, Bah S, Bahadorikhalili S, Bai J, Bai R, Baker JL, Bakkannavar SM, Bako AT, Balakrishnan S, Balogun SA, Baltatu OC, Bam K, Banach M, Bandyopadhyay S, Banik B, Banik PC, Bansal H, Barati S, Barchitta M, Bardhan M, Barker-Collo SL, Barone-Adesi F, Barqawi HJ, Barr RD, Barrero LH, Basharat Z, Bashir AIJ, Bashiru HA, Baskaran P, Basnyat B, Bassat Q, Basso JD, Basu S, Batra K, Batra R, Baune BT, Bayati M, Bayileyegn NS, Beaney T, Bedi N, Begum T, Behboudi E, Behnoush AH, Beiranvand M, Bejarano Ramirez DF, Belgaumi UI, Bell ML, Bello AK, Bello MB, Bello OO, Belo L, Beloukas A, Bendak S, Bennett DA, Bensenor IM, Benzian H, Berezvai Z, Berman AE, Bermudez ANC, Bettencourt PJG, Beyene HB, Beyene KA, Bhagat DS, Bhagavathula AS, Bhala N, Bhalla A, Bhandari D, Bhardwaj N, Bhardwaj P, Bhardwaj PV, Bhargava A, Bhaskar S, Bhat V, Bhatti GK, Bhatti JS, Bhatti MS, Bhatti R, Bhutta ZA, Bikbov B, Binmadi N, Bintoro BS, Biondi A, Bisignano C, Bisulli F, Biswas A, Biswas RK, Bitaraf S, Bjørge T, Bleyer A, Boampong MS, Bodolica V, Bodunrin AO, Bolarinwa OA, Bonakdar Hashemi M, Bonny A, Bora K, Bora Basara B, Borodo SB, Borschmann R, Botero Carvajal A, Bouaoud S, Boudalia S, Boyko EJ, Bragazzi NL, Braithwaite D, Brenner H, Britton G, Browne AJ, Brunoni AR, Bulamu NB, Bulto LN, Buonsenso D, Burkart K, Burns RA, Burugina Nagaraja S, Busse R, Bustanji Y, Butt ZA, Caetano dos Santos FL, Cai T, Calina D, Cámera LA, Campos LA, Campos-Nonato IR, Cao C, Cardenas CA, Cárdenas R, Carr S, Carreras G, Carrero JJ, Carugno A, Carvalho F, Carvalho M, Castaldelli-Maia 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Abdulkader R, Sultana A, Sun J, Sunkersing D, Susanty S, Swain CK, Sykes BL, Szarpak L, Szeto MD, Szócska M, Tabaee Damavandi P, Tabatabaei Malazy O, Tabatabaeizadeh SA, Tabatabai S, Tabb KM, Tabish M, Taborda-Barata LM, Tabuchi T, Tadesse BT, Taheri A, Taheri Abkenar Y, Taheri Soodejani M, Taherkhani A, Taiba J, Tajbakhsh A, Talaat IM, Talukder A, Tamuzi JL, Tan KK, Tang H, Tang HK, Tat NY, Tat VY, Tavakoli Oliaee R, Tavangar SM, Taveira N, Tebeje TM, Tefera YM, Teimoori M, Temsah MH, Temsah RMH, Teramoto M, Tesfaye SH, Thangaraju P, Thankappan KR, Thapa R, Thapar R, Thomas N, Thrift AG, Thum CCC, Tian J, Tichopad A, Ticoalu JHV, Tiruye TY, Tohidast SA, Tonelli M, Touvier M, Tovani-Palone MR, Tram KH, Tran NM, Trico D, Trihandini I, Tromans SJ, Truong VT, Truyen TTTT, Tsermpini EE, Tumurkhuu M, Tung K, Tyrovolas S, Ubah CS, Udoakang AJ, Udoh A, Ulhaq I, Ullah S, Ullah S, Umair M, Umar TP, Umeokonkwo CD, Umesh A, Unim B, Unnikrishnan B, Upadhyay E, Urso D, Vacante M, Vahdani AM, Vaithinathan AG, Valadan Tahbaz S, Valizadeh R, Van den Eynde J, Varavikova E, Varga O, Varma SA, Vart P, Varthya SB, Vasankari TJ, Veerman LJ, Venketasubramanian N, Venugopal D, Verghese NA, Verma M, Verma P, Veroux M, Verras GI, Vervoort D, Vieira RJ, Villafañe JH, Villani L, Villanueva GI, Villeneuve PJ, Violante FS, Visontay R, Vlassov V, Vo B, Vollset SE, Volovat SR, Volovici V, Vongpradith A, Vos T, Vujcic IS, Vukovic R, Wado YD, Wafa HA, Waheed Y, Wamai RG, Wang C, Wang D, Wang F, Wang S, Wang S, Wang Y, Wang YP, Ward P, Watson S, Weaver MR, Weerakoon KG, Weiss DJ, Weldemariam AH, Wells KM, Wen YF, Werdecker A, Westerman R, Wickramasinghe DP, Wickramasinghe ND, Wijeratne T, Wilson S, Wojewodzic MW, Wool EE, Woolf AD, Wu D, Wulandari RD, Xiao H, Xu B, Xu X, Yadav L, Yaghoubi S, Yang L, Yano Y, Yao Y, Ye P, Yesera GE, Yesodharan R, Yesuf SA, Yiğit A, Yiğit V, Yip P, Yon DK, Yonemoto N, You Y, Younis MZ, Yu C, Zadey S, Zadnik V, Zafari N, Zahedi M, Zahid MN, Zahir M, Zakham F, Zaki N, Zakzuk J, Zamagni G, Zaman BA, Zaman SB, Zamora N, Zand R, Zandi M, Zandieh GGZ, Zanghì A, Zare I, Zastrozhin MS, Zeariya MGM, Zeng Y, Zhai C, Zhang C, Zhang H, Zhang H, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao Y, Zhao Y, Zheng P, Zhong C, Zhou J, Zhu B, Zhu Z, Ziaeefar P, Zielińska M, Zou Z, Zumla A, Zweck E, Zyoud SH, Lim SS, Murray CJL. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00476-8. [PMID: 38484753 DOI: 10.1016/s0140-6736(24)00476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/08/2023] [Accepted: 03/06/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period. METHODS 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING Bill & Melinda Gates Foundation.
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Osman AM, Toson B, Naik GR, Mukherjee S, Delbeck M, Hahn M, Muller T, Weimann G, Eckert DJ. A novel TASK channel antagonist nasal spray reduces sleep apnea severity in physiological responders: a randomized, blinded, trial. Am J Physiol Heart Circ Physiol 2024; 326:H715-H723. [PMID: 38214905 DOI: 10.1152/ajpheart.00541.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 12/25/2023] [Accepted: 12/28/2023] [Indexed: 01/13/2024]
Abstract
Preclinical and human physiological studies indicate that topical, selective TASK 1/3 K+ channel antagonism increases upper airway dilator muscle activity and reduces pharyngeal collapsibility during anesthesia and nasal breathing during sleep. The primary aim of this study was to determine the effects of BAY2586116 nasal spray on obstructive sleep apnea (OSA) severity and whether individual responses vary according to differences in physiological responses and route of breathing. Ten people (5 females) with OSA [apnea-hypopnea index (AHI) = 47 ± 26 events/h (means ± SD)] who completed previous sleep physiology studies with BAY2586116 were invited to return for three polysomnography studies to quantify OSA severity. In random order, participants received either placebo nasal spray (saline), BAY2586116 nasal spray (160 µg), or BAY2586116 nasal spray (160 µg) restricted to nasal breathing (chinstrap or mouth tape). Physiological responders were defined a priori as those who had improved upper airway collapsibility (critical closing pressure ≥2 cmH2O) with BAY2586116 nasal spray (NCT04236440). There was no systematic change in apnea-hypopnea index (AHI3) from placebo versus BAY2586116 with either unrestricted or nasal-only breathing versus placebo (47 ± 26 vs. 43 ± 27 vs. 53 ± 33 events/h, P = 0.15). However, BAY2586116 (unrestricted breathing) reduced OSA severity in physiological responders compared with placebo (e.g., AHI3 = 28 ± 11 vs. 36 ± 12 events/h, P = 0.03 and ODI3 = 18 ± 10 vs. 28 ± 12 events/h, P = 0.02). Morning blood pressure was also lower in physiological responders after BAY2586116 versus placebo (e.g., systolic blood pressure = 137 ± 24 vs. 147 ± 21 mmHg, P < 0.01). In conclusion, BAY2586116 reduces OSA severity during sleep in people who demonstrate physiological improvement in upper airway collapsibility. These findings highlight the therapeutic potential of this novel pharmacotherapy target in selected individuals.NEW & NOTEWORTHY Preclinical findings in pigs and humans indicate that blocking potassium channels in the upper airway with topical nasal application increases pharyngeal dilator muscle activity and reduces upper airway collapsibility. In this study, BAY2586116 nasal spray (potassium channel blocker) reduced sleep apnea severity in those who had physiological improvement in upper airway collapsibility. BAY2586116 lowered the next morning's blood pressure. These findings highlight the potential for this novel therapeutic approach to improve sleep apnea in certain people.
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Affiliation(s)
- Amal M Osman
- Adelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Barbara Toson
- Adelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Ganesh R Naik
- Adelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
| | - Sutapa Mukherjee
- Adelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
- Respiratory and Sleep Service, Southern Adelaide Local Health Network, SA Health, Adelaide, South Australia, Australia
| | - Martina Delbeck
- Research & Development, Bayer AG, Pharmaceuticals, Wuppertal, Germany
| | - Michael Hahn
- Research & Development, Bayer AG, Pharmaceuticals, Wuppertal, Germany
| | - Thomas Muller
- Research & Development, Bayer AG, Pharmaceuticals, Wuppertal, Germany
| | - Gerrit Weimann
- Research & Development, Bayer AG, Pharmaceuticals, Wuppertal, Germany
| | - Danny J Eckert
- Adelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
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Shaikh AAS, Bhargavi MS, Naik GR. Unraveling the complexities of pathological voice through saliency analysis. Comput Biol Med 2023; 166:107566. [PMID: 37857135 DOI: 10.1016/j.compbiomed.2023.107566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/14/2023] [Accepted: 10/10/2023] [Indexed: 10/21/2023]
Abstract
The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice pathology disorders can be effectively detected using Artificial Intelligence and computer-aided voice pathology classification tools. Previous studies focused primarily on binary classification, leaving limited attention to multi-class classification. This study proposes three different neural network architectures to investigate the feature characteristics of three voice pathologies-Hyperkinetic Dysphonia, Hypokinetic Dysphonia, Reflux Laryngitis, and healthy voices using multi-class classification and the Voice ICar fEDerico II (VOICED) dataset. The study proposes UNet++ autoencoder-based denoiser techniques for accurate feature extraction to overcome noisy data. The architectures include a Multi-Layer Perceptron (MLP) trained on structured feature sets, a Short-Time Fourier Transform (STFT) model, and a Mel-Frequency Cepstral Coefficients (MFCC) model. The MLP model on 143 features achieved 97.1% accuracy, while the STFT model showed similar performance with increased sensitivity of 99.8%. The MFCC model maintained 97.1% accuracy but with a smaller model size and improved accuracy on the Reflux Laryngitis class. The study identifies crucial features through saliency analysis and reveals that detecting voice abnormalities requires the identification of regions of inaudible high-pitch sounds. Additionally, the study highlights the challenges posed by limited and disjointed pathological voice databases and proposes solutions for enhancing the performance of voice abnormality classification. Overall, the study's findings have potential applications in clinical applications and specialized audio-capturing tools.
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Affiliation(s)
- Abdullah Abdul Sattar Shaikh
- Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, 560004, Karnataka, India.
| | - M S Bhargavi
- Department of Computer Science and Engineering, Bangalore Institute of Technology, Bangalore, 560004, Karnataka, India.
| | - Ganesh R Naik
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park 5042, Adelaide, SA, Australia.
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Brandwood BM, Naik GR, Gunawardana U, Gargiulo GD. Combined Cardiac and Respiratory Monitoring from a Single Signal: A Case Study Employing the Fantasia Database. Sensors (Basel) 2023; 23:7401. [PMID: 37687857 PMCID: PMC10490584 DOI: 10.3390/s23177401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/16/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023]
Abstract
This study proposes a novel method for obtaining the electrocardiogram (ECG) derived respiration (EDR) from a single lead ECG and respiration-derived cardiogram (RDC) from a respiratory stretch sensor. The research aims to reconstruct the respiration waveform, determine the respiration rate from ECG QRS heartbeat complexes data, locate heartbeats, and calculate a heart rate (HR) using the respiration signal. The accuracy of both methods will be evaluated by comparing located QRS complexes and inspiration maxima to reference positions. The findings of this study will ultimately contribute to the development of new, more accurate, and efficient methods for identifying heartbeats in respiratory signals, leading to better diagnosis and management of cardiovascular diseases, particularly during sleep where respiration monitoring is paramount to detect apnoea and other respiratory dysfunctions linked to a decreased life quality and known cause of cardiovascular diseases. Additionally, this work could potentially assist in determining the feasibility of using simple, no-contact wearable devices for obtaining simultaneous cardiology and respiratory data from a single device.
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Affiliation(s)
- Benjamin M. Brandwood
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia;
| | - Upul Gunawardana
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia;
- The MARCS Institute, Westmead, NSW 2145, Australia
- Translational Research Health Institute, Westmead, NSW 2145, Australia
- The Ingam Institute for Medical Research, Liverpool, NSW 2170, Australia
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Naik GR, Breen PP, Jayarathna T, Tong BK, Eckert DJ, Gargiulo GD. Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography. Biosensors (Basel) 2023; 13:703. [PMID: 37504102 PMCID: PMC10377422 DOI: 10.3390/bios13070703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/19/2023] [Accepted: 06/26/2023] [Indexed: 07/29/2023]
Abstract
Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze. This work compares the performance of a non-invasive wearable stretchable morphic sensor, which does not require direct skin contact, embedded in a t-shirt worn by 32 volunteer participants (26 males, 6 females) with sleep-disordered breathing who performed a detailed, overnight in-laboratory sleep study. Direct comparison of computed respiratory parameters from morphic sensors versus traditional polysomnography had approximately 95% (95 ± 0.7) accuracy. These findings confirm that novel wearable morphic sensors provide a viable alternative to non-invasively and simultaneously capture respiratory rate and chest and abdominal motions.
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Affiliation(s)
- Ganesh R Naik
- Adelaide Institute for Sleep Health (Flinders Health and Medical Research Institute: Sleep Health), College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia
- College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - Paul P Breen
- The MARCS Institute, Western Sydney University, Westmead, NSW 2145, Australia
| | - Titus Jayarathna
- The MARCS Institute, Western Sydney University, Westmead, NSW 2145, Australia
| | - Benjamin K Tong
- Neuroscience Research Australia, Randwick, NSW 2031, Australia
- Sleep Research Group, Charles Perkins Centre, School of Medicine, University of Sydney, Camperdown, NSW 2006, Australia
| | - Danny J Eckert
- Adelaide Institute for Sleep Health (Flinders Health and Medical Research Institute: Sleep Health), College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia
- Neuroscience Research Australia, Randwick, NSW 2031, Australia
| | - Gaetano D Gargiulo
- The MARCS Institute, Western Sydney University, Westmead, NSW 2145, Australia
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2751, Australia
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dos Santos WP, Conti V, Gambino O, Naik GR. Editorial: Machine learning and applied neuroscience. Front Neurorobot 2023; 17:1191045. [PMID: 37091067 PMCID: PMC10117933 DOI: 10.3389/fnbot.2023.1191045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 03/27/2023] [Indexed: 04/09/2023] Open
Affiliation(s)
- Wellington Pinheiro dos Santos
- Department of Biomedical Engineering, Federal University of Pernambuco, Recife, Brazil
- *Correspondence: Wellington Pinheiro dos Santos
| | - Vincenzo Conti
- Faculty of Engineering and Architecture, Informatics Engineering, University of Enna Kore, Enna, Italy
| | - Orazio Gambino
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia
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Gams A, Naik GR. Editorial: Neurorobotics explores gait movement in the sporting community. Front Neurorobot 2023; 17:1127994. [PMID: 36733372 PMCID: PMC9887332 DOI: 10.3389/fnbot.2023.1127994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/04/2023] [Indexed: 01/18/2023] Open
Affiliation(s)
- Andrej Gams
- Humanoid and Cognitive Robotics Lab, Department of Automatics, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia,*Correspondence: Andrej Gams ✉
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
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Bourges M, Naik GR, Mesin L. Single channel surface electromyogram deconvolution is a useful pre-processing for myoelectric control. IEEE Trans Biomed Eng 2021; 69:1767-1775. [PMID: 34847017 DOI: 10.1109/tbme.2021.3131650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Myoelectric control requires fast and stable identification of a movement from data recorded from a comfortable and straightforward system. Here we consider a new real-time pre-processing method applied to a single differential surface electromyogram (EMG): deconvolution, providing an estimation of the cumulative firings of motor units. A 2 channel-10 class finger movement problem has been investigated on 10 healthy subjects. We have compared raw EMG and deconvolution signals, as sources of information for two specific classifiers (based on either Support Vector Machines or k-Nearest Neighbours), with classical time-domain input features selected using Mutual Component Analysis. The overall results show that, using the proposed pre-processing technique, classification performances statistically improve. For example, the true positive rates of the best-tested configurations were 80.9% and 86.3% when using the EMG and its deconvoluted signal, respectively. Even considering the limited dataset and range of classification approaches investigated, these preliminary results indicate the potential usefulness of the deconvolution pre-processing, which could be easily embedded in different myoelectric control applications.
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Esposito D, Centracchio J, Andreozzi E, Gargiulo GD, Naik GR, Bifulco P. Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey. Sensors (Basel) 2021; 21:s21206863. [PMID: 34696076 PMCID: PMC8540117 DOI: 10.3390/s21206863] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/30/2021] [Accepted: 10/12/2021] [Indexed: 12/03/2022]
Abstract
As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.
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Affiliation(s)
- Daniele Esposito
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
| | - Jessica Centracchio
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
| | - Emilio Andreozzi
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
| | - Gaetano D. Gargiulo
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia;
- The MARCS Institute, Western Sydney University, Penrith, NSW 2751, Australia
| | - Ganesh R. Naik
- School of Engineering, Design and Built Environment, Western Sydney University, Penrith, NSW 2747, Australia;
- The Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA 5042, Australia
- Correspondence:
| | - Paolo Bifulco
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples “Federico II”, 80125 Naples, Italy; (D.E.); (J.C.); (E.A.); (P.B.)
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Amoghavarsha C, Pramesh D, Naik GR, Naik MK, Yadav MK, Ngangkham U, Chidanandappa E, Raghunandana A, Sharanabasav H, E Manjunatha S. Morpho-molecular diversity and avirulence genes distribution among the diverse isolates of Magnaporthe oryzae from Southern India. J Appl Microbiol 2021; 132:1275-1290. [PMID: 34327783 DOI: 10.1111/jam.15243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/23/2021] [Accepted: 07/26/2021] [Indexed: 11/26/2022]
Abstract
AIMS To investigate the diversity of eco-distinct isolates of Magnaporthe oryzae for their morphological, virulence and molecular diversity and relative distribution of five Avr genes. METHODS AND RESULTS Fifty-two M. oryzae isolates were collected from different rice ecosystems of southern India. A majority of them (n = 28) formed a circular colony on culture media. Based on the disease reaction on susceptible cultivar (cv. HR-12), all 52 isolates were classified in to highly virulent (n = 28), moderately virulent (n = 11) and less-virulent (13) types. Among the 52 isolates, 38 were selected for deducing internal transcribed spacer (ITS) sequence diversity. For deducing phylogeny, another set of 36 isolates from other parts of the world was included, which yielded two distinct phylogenetic clusters. We identified eight haplotype groups and 91 variable sites within the ITS sequences, and haplotype-group-2 (Hap_2) was predominant (n = 24). The Tajima's and Fu's Fs neutrality tests exhibited many rare alleles. Furthermore, PCR analysis for detecting the presence of five Avr genes in the different M. oryzae isolates using Avr gene-specific primers in PCR revealed that Avr-Piz-t, Avr-Pik, Avr-Pia and Avr-Pita were present in 73.68%, 73.68%, 63.16% and 47.37% of the isolates studied, respectively; whereas, Avr-Pii was identified only in 13.16% of the isolates. CONCLUSIONS Morpho-molecular and virulence studies revealed the significant diversity among eco-distinct isolates. PCR detection of Avr genes among the M. oryzae population revealed the presence of five Avr genes. Among them, Avr-Piz-t, Avr-Pik and Avr-Pia were more predominant. SIGNIFICANCE AND IMPACT OF THE STUDY The study documented the morphological and genetic variability of eco-distinct M. oryzae isolates. This is the first study demonstrating the distribution of the Avr genes among the eco-distinct population of M. oryzae from southern India. The information generated will help plant breeders to select appropriate resistant gene/s combinations to develop blast disease-resistant rice cultivars.
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Affiliation(s)
- Chittaragi Amoghavarsha
- Department of Plant Pathology, University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, India.,Rice Pathology Laboratory, All India Coordinated Rice Improvement Programme, University of Agricultural Sciences, Raichur, Karnataka, India
| | - Devanna Pramesh
- Rice Pathology Laboratory, All India Coordinated Rice Improvement Programme, University of Agricultural Sciences, Raichur, Karnataka, India
| | - Ganesh R Naik
- Department of Plant Pathology, University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, India
| | - Manjunath K Naik
- Department of Plant Pathology, University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, India
| | - Manoj K Yadav
- ICAR-National Rice Research Institute, Cuttack, India
| | - Umakanta Ngangkham
- ICAR-Research Complex for North-Eastern Hill Region, Manipur center, Imphal, Manipur, India
| | - Eranna Chidanandappa
- Rice Pathology Laboratory, All India Coordinated Rice Improvement Programme, University of Agricultural Sciences, Raichur, Karnataka, India
| | - Adke Raghunandana
- Rice Pathology Laboratory, All India Coordinated Rice Improvement Programme, University of Agricultural Sciences, Raichur, Karnataka, India
| | - Huded Sharanabasav
- Rice Pathology Laboratory, All India Coordinated Rice Improvement Programme, University of Agricultural Sciences, Raichur, Karnataka, India
| | - Siddepalli E Manjunatha
- Rice Pathology Laboratory, All India Coordinated Rice Improvement Programme, University of Agricultural Sciences, Raichur, Karnataka, India
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17
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Saha S, Mamun KA, Ahmed K, Mostafa R, Naik GR, Darvishi S, Khandoker AH, Baumert M. Progress in Brain Computer Interface: Challenges and Opportunities. Front Syst Neurosci 2021; 15:578875. [PMID: 33716680 PMCID: PMC7947348 DOI: 10.3389/fnsys.2021.578875] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 01/06/2021] [Indexed: 12/13/2022] Open
Abstract
Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.
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Affiliation(s)
- Simanto Saha
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Khondaker A. Mamun
- Advanced Intelligent Multidisciplinary Systems (AIMS) Lab, Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh
| | - Khawza Ahmed
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Raqibul Mostafa
- Department of Electrical and Electronic Engineering, United International University, Dhaka, Bangladesh
| | - Ganesh R. Naik
- Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Sam Darvishi
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Ahsan H. Khandoker
- Healthcare Engineering Innovation Center, Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
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18
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Gautam A, Panwar M, Wankhede A, Arjunan SP, Naik GR, Acharyya A, Kumar DK. Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control. IEEE J Transl Eng Health Med 2020; 8:2100812. [PMID: 33014638 PMCID: PMC7529116 DOI: 10.1109/jtehm.2020.3023898] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 08/18/2020] [Accepted: 08/30/2020] [Indexed: 02/06/2023]
Abstract
Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named 'Low-Complex Movement recognition-Net' (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets- our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin- Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT's), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and low-complex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons.
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Affiliation(s)
- Arvind Gautam
- Indian Institute of Technology HyderabadHyderabad502205India
| | - Madhuri Panwar
- Indian Institute of Technology HyderabadHyderabad502205India
| | | | | | | | - Amit Acharyya
- Indian Institute of Technology HyderabadHyderabad502205India
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19
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Ghosh SK, Tripathy RK, Paternina MRA, Arrieta JJ, Zamora-Mendez A, Naik GR. Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network. J Med Syst 2020; 44:114. [PMID: 32388733 DOI: 10.1007/s10916-020-01565-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 03/31/2020] [Indexed: 12/14/2022]
Abstract
Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.
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Affiliation(s)
- S K Ghosh
- MLR Institute of Technology, Hyderabad, India
| | - R K Tripathy
- Birla Institute of Technology and Science Pilani, Hyderabad, India.
| | - Mario R A Paternina
- National Autonomous University of Mexico (UNAM), Mexico City, Mex. 04510, Mexico
| | | | | | - Ganesh R Naik
- Biomedical Engineering and Neuromorphic Systems (BENS) Research Group, MARCS Institute, Western Sydney University, Penrith, New South Wales, Australia
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20
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Esposito D, Andreozzi E, Gargiulo GD, Fratini A, D'Addio G, Naik GR, Bifulco P. A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition. Front Neurorobot 2020; 13:114. [PMID: 32009926 PMCID: PMC6978746 DOI: 10.3389/fnbot.2019.00114] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 12/17/2019] [Indexed: 11/28/2022] Open
Abstract
Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control.
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Affiliation(s)
- Daniele Esposito
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy.,Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Emilio Andreozzi
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy.,Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Gaetano D Gargiulo
- School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia
| | - Antonio Fratini
- School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Giovanni D'Addio
- Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Ganesh R Naik
- MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | - Paolo Bifulco
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy.,Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
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21
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Maheshwari K, Joseph Raj AN, Mahesh VG, Zhuang Z, Rufus E, Shivakumara P, Naik GR. Bilingual text detection in natural scene images using invariant moments. IFS 2019. [DOI: 10.3233/jifs-190339] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
| | - Alex Noel Joseph Raj
- Department of Electronic Engineering, Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou, China
| | - Vijayalakshmi G.V. Mahesh
- Department of Electronics and Communication Engineering, BMS Institute of Technology and Management, Bangalore, Karnataka, India
| | - Zhemin Zhuang
- Department of Electronic Engineering, Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou, China
| | | | - Palaiahnakote Shivakumara
- Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lampur, Malaysia
| | - Ganesh R. Naik
- MARCS Institute, Western Sydney University, Sydney, Australia
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22
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Naik GR, Gargiulo GD, Serrador JM, Breen PP. Groundtruth: A Matlab GUI for Artifact and Feature Identification in Physiological Signals. Front Physiol 2019; 10:850. [PMID: 31481893 PMCID: PMC6710362 DOI: 10.3389/fphys.2019.00850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 06/20/2019] [Indexed: 12/03/2022] Open
Abstract
Groundtruth is a Matlab Graphical User Interface (GUI) developed for the identification of key features and artifacts within physiological signals. The ultimate aim of this GUI is to provide a simple means of assessing the performance of new sensors. Secondary, to this is providing a means of providing marked data, enabling assessment of automated artifact rejection and feature identification algorithms. With the emergence of new wearable sensor technologies, there is an unmet need for convenient assessment of device performance, and a faster means of assessing new algorithms. The proposed GUI allows interactive marking of artifact regions as well as simultaneous interactive identification of key features, e.g., respiration peaks in respiration signals, R-peaks in Electrocardiography signals, etc. In this paper, we present the base structure of the system, together with an example of its use for two simultaneously worn respiration sensors. The respiration rates are computed for both original as well as artifact removed data and validated using Bland–Altman plots. The respiration rates computed based on the proposed GUI (after artifact removal process) demonstrated consistent results for two respiration sensors after artifact removal process. Groundtruth is customizable, and alternative processing modules are easy to add/remove. Groundtruth is intended for open-source use.
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Affiliation(s)
- Ganesh R Naik
- Biomedical Engineering and Neuromorphic Systems, The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | - Gaetano D Gargiulo
- Biomedical Engineering and Neuromorphic Systems, The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia
| | - Jorge M Serrador
- Rutgers Biomedical and Health Sciences, Newark, NJ, United States.,Department of Pharmacology, Physiology & Neuroscience, New Jersey Medical School, Rutgers University, Newark, NJ, United States
| | - Paul P Breen
- Biomedical Engineering and Neuromorphic Systems, The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia.,Translational Health Research Institute, Western Sydney University, Penrith, NSW, Australia
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23
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Tripathy RK, Paternina MRA, Arrieta JG, Zamora-Méndez A, Naik GR. Automated detection of congestive heart failure from electrocardiogram signal using Stockwell transform and hybrid classification scheme. Comput Methods Programs Biomed 2019; 173:53-65. [PMID: 31046996 DOI: 10.1016/j.cmpb.2019.03.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2018] [Revised: 02/12/2019] [Accepted: 03/13/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE The congestive heart failure (CHF) is a life-threatening cardiac disease which arises when the pumping action of the heart is less than that of the normal case. This paper proposes a novel approach to design a classifier-based system for the automated detection of CHF. METHODS The approach is founded on the use of the Stockwell (S)-transform and frequency division to analyze the time-frequency sub-band matrices stemming from electrocardiogram (ECG) signals. Then, the entropy features are evaluated from the sub-band matrices of ECG. A hybrid classification scheme is adopted taking the sparse representation classifier and the average of the distances from the nearest neighbors into account for the detection of CHF. The proposition is validated using ECG signals from CHF subjects and normal sinus rhythm from public databases. RESULTS The results reveal that the proposed system is successful for the detection of CHF with an accuracy, a sensitivity and a specificity values of 98.78%, 98.48%, and 99.09%, respectively. A comparison with the existing approaches for the detection of CHF is accomplished. CONCLUSIONS The time-frequency entropy features of the ECG signal in the frequency range from 11 Hz to 30 Hz have higher performance for the detection of CHF using a hybrid classifier. The approach can be used for the automated detection of CHF in tele-healthcare monitoring systems.
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Affiliation(s)
- R K Tripathy
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India.
| | - Mario R A Paternina
- Department of Electrical Engineering, National Autonomous University of Mexico, Mexico City, 04510, Mexico
| | | | - Alejandro Zamora-Méndez
- Electrical Engineering Faculty, Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Mich. 58030, Mexico
| | - Ganesh R Naik
- MARCS Institute, Western Sydney University Kingswood, NSW - 2747, Australia
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24
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Naik GR, Selvan SE, Arjunan SP, Acharyya A, Kumar DK, Ramanujam A, Nguyen HT. An ICA-EBM-Based sEMG Classifier for Recognizing Lower Limb Movements in Individuals With and Without Knee Pathology. IEEE Trans Neural Syst Rehabil Eng 2019. [PMID: 29522411 DOI: 10.1109/tnsre.2018.2796070] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.
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25
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Ardi Handojoseno AM, Gilat M, Ehgoetz Martens KA, Georgiades M, Naik GR, Tran Y, Lewis SJG, Nguyen HT. Detection of turning freeze in Parkinson's disease based on S-transform decomposition of EEG signals. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2017:3044-3047. [PMID: 29060540 DOI: 10.1109/embc.2017.8037499] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Freezing of Gait (FOG) is a highly debilitating and poorly understood symptom of Parkinson's disease (PD), causing severe immobility and decreased quality of life. Turning Freezing (TF) is known as the most common sub-type of FOG, also causing the highest rate of falls in PD patients. During a TF, the feet of PD patients appear to become stuck whilst making a turn. This paper presents an electroencephalography (EEG) based classification method for detecting turning freezing episodes in six PD patients during Timed Up and Go Task experiments. Since EEG signals have a time-variant nature, time-frequency Stockwell Transform (S-Transform) techniques were used for feature extraction. The EEG sources were separated by means of independent component analysis using entropy bound minimization (ICA-EBM). The distinctive frequency-based features of selected independent components of EEG were extracted and classified using Bayesian Neural Networks. The classification demonstrated a high sensitivity of 84.2%, a specificity of 88.0% and an accuracy of 86.2% for detecting TF. These promising results pave the way for the development of a real-time device for detecting different sub-types of FOG during ambulation.
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26
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Ly QT, Ardi Handojoseno AM, Gilat M, Chai R, Ehgoetz Martens KA, Georgiades M, Naik GR, Tran Y, Lewis SJG, Nguyen HT. Detection of gait initiation Failure in Parkinson's disease based on wavelet transform and Support Vector Machine. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2017:3048-3051. [PMID: 29060541 DOI: 10.1109/embc.2017.8037500] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Gait initiation Failure (GIF) is the situation in which patients with Parkinson's disease (PD) feel as if their feet get "stuck" to the floor when initiating their first steps. GIF is a subtype of Freezing of Gait (FOG) and often leads to falls and related injuries. Understanding of neurobiological mechanisms underlying GIF has been limited by difficulties in eliciting and objectively characterizing such gait phenomena in the clinical setting. Studies investigating the effects of GIF on brain activity using EEG offer the potential to study such behavior. In this preliminary study, we present a novel methodology where wavelet transform was used for feature extraction and Support Vector Machine for classifying GIF events in five patients with PD and FOG. To deal with the large amount of EEG data, a Principal Component Analysis (PCA) was applied to reduce the data dimension from 15 EEG channels into 6 principal components (PCs), retaining 93% of the information. Independent Component Analysis using Entropy Bound Minimization (ICA-EBM) was applied to 6 PCs for source separation with the aim of improving detection ability of GIF events as compared to the normal initiation of gait (Good Starts). The results of this analysis demonstrated the correct identification of GIF episodes with an 83.1% sensitivity, 89.5% specificity and 86.3% accuracy. These results suggest that our proposed methodology is a promising non-invasive approach to improve GIF detection in PD and FOG.
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27
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Yu H, Ye L, Naik GR, Song R, Nguyen HT, Su SW. Nonparametric dynamical model of cardiorespiratory responses at the onset and offset of treadmill exercises. Med Biol Eng Comput 2018; 56:2337-2351. [PMID: 29956216 DOI: 10.1007/s11517-018-1860-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Accepted: 06/11/2018] [Indexed: 10/28/2022]
Abstract
This paper applies a nonparametric modelling method with kernel-based regularization to estimate the carbon dioxide production during jogging exercises. The kernel selection and regularization strategies have been discussed; several commonly used kernels are compared regarding the goodness-of-fit, sensitivity, and stability. Based on that, the most appropriate kernel is then selected for the construction of the regularization term. Both the onset and offset of the jogging exercises are investigated. We compare the identified nonparametric models, which include both impulse response models and step response models for the two periods, as well as the relationship between oxygen consumption and carbon dioxide production. The result statistically indicates that the steady-state gain of the carbon dioxide production in the onset of exercise is bigger than that in the offset while the response time of both onset and offset are similar. Compared with oxygen consumption, the response speed of carbon dioxide production is slightly slower in both onset and offset period while its steady-state gains are similar for both periods. The effectiveness of the kernel-based method for the dynamic modelling of cardiorespiratory response to exercise is also well demonstrated. Graphical Abstract Comparison between VO2 and VCO2 during onset and offset of exercise.
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Affiliation(s)
- Hairong Yu
- School of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Lin Ye
- School of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Ganesh R Naik
- Marcs Institute For Brain, Behaviour & Development, Western Sydney University, Sydney, NSW, 2751, Australia
| | - Rong Song
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Hung T Nguyen
- Faculty of Science, Engineering & Technology, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Steven W Su
- School of Biomedical Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia.
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28
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Tripathy RK, Zamora-Mendez A, de la O Serna JA, Paternina MRA, Arrieta JG, Naik GR. Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform. Front Physiol 2018; 9:722. [PMID: 29951004 PMCID: PMC6008495 DOI: 10.3389/fphys.2018.00722] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 05/24/2018] [Indexed: 11/13/2022] Open
Abstract
Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated from the mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.
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Affiliation(s)
- Rajesh K Tripathy
- Faculty of Engineering and Technology (ITER), Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, India
| | - Alejandro Zamora-Mendez
- Electrical Engineering Faculty, Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Mexico
| | - José A de la O Serna
- Department of Electrical Engineering, Autonomous University of Nuevo León, Monterrey, Mexico
| | | | | | - Ganesh R Naik
- Biomedical Engineering and Neuromorphic Systems (BENS) Research Group, MARCS Institute, Western Sydney University, Penrith, NSW, Australia
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Acharyya A, Jadhav PN, Bono V, Maharatna K, Naik GR. Low-complexity hardware design methodology for reliable and automated removal of ocular and muscular artifact from EEG. Comput Methods Programs Biomed 2018; 158:123-133. [PMID: 29544778 DOI: 10.1016/j.cmpb.2018.02.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 12/31/2017] [Accepted: 02/02/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE EEG is a non-invasive tool for neuro-developmental disorder diagnosis and treatment. However, EEG signal is mixed with other biological signals including Ocular and Muscular artifacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners which may result in less accurate diagnosis. Many existing methods require reference electrodes, which will create discomfort to the patient/children and cause hindrance to the diagnosis of the neuro-developmental disorder and Brain Computer Interface in the pervasive environment. Therefore, it would be ideal if these artifacts can be removed real time on the hardware platform in an automated fashion and then the denoised EEG can be used for online diagnosis in a pervasive personalized healthcare environment without the need of any reference electrode. METHODS In this paper we propose a reliable, robust and automated methodology to solve the aforementioned problem. The proposed methodology is based on the Haar function based Wavelet decompositions with simple threshold based wavelet domain denoising and artifacts removal schemes. Subsequently hardware implementation results are also presented. 100 EEG data from Physionet, Klinik für Epileptologie, Universität Bonn, Germany, Caltech EEG databases and 7 EEG data from 3 subjects from University of Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated data have been formulated and tested. The proposed methodology is prototyped and validated using FPGA platform. RESULTS Like existing literature, the performance of the proposed methodology is also measured in terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and 65% with the gain in hardware complexity of 64.28% and improvement in hardware delay of 53.58% compared to state-of-the art approaches. Hardware design based on the proposed methodology consumes 75 micro-Watt power. CONCLUSIONS The automated methodology proposed in this paper, unlike the state of the art methods, can remove blink and muscular artifacts real time without the need of any extra electrode. Its reliability and robustness is also established after exhaustive simulation study and analysis on both simulated and real data. We believe the proposed methodology would be useful in next generation personalized pervasive healthcare for Brain Computer Interface and neuro-developmental disorder diagnosis and treatment.
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Affiliation(s)
- Amit Acharyya
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India.
| | - Pranit N Jadhav
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India.
| | - Valentina Bono
- School of Electronic & Computer Science, University of Southampton, Southampton, UK.
| | - Koushik Maharatna
- School of Electronic & Computer Science, University of Southampton, Southampton, UK.
| | - Ganesh R Naik
- MARCS Institute Western Sydney University Kingswood, NSW - 2747, Australia.
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Handojoseno AMA, Naik GR, Gilat M, Shine JM, Nguyen TN, Ly QT, Lewis SJG, Nguyen HT. Prediction of Freezing of Gait in Patients with Parkinson's Disease Using EEG Signals. Stud Health Technol Inform 2018; 246:124-131. [PMID: 29507265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Freezing of gait (FOG) is an episodic gait disturbance affecting initiation and continuation of locomotion in many Parkinson's disease (PD) patients, causing falls and a poor quality of life. FOG can be experienced on turning and start hesitation, passing through doorways or crowded areas dual tasking, and in stressful situations. Electroencephalography (EEG) offers an innovative technique that may be able to effectively foresee an impending FOG. From data of 16 PD patients, using directed transfer function (DTF) and independent component analysis (ICA) as data pre-processing, and an optimal Bayesian neural network as a predictor of a transition of 5 seconds before the impending FOG occurs in 11 in-group PD patients, we achieved sensitivity and specificity of 85.86% and 80.25% respectively in the test set (5 out-group PD patients). This study therefore contributes to the development of a non-invasive device to prevent freezing of gait in PD.
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Affiliation(s)
- A M Ardi Handojoseno
- Faculty of Science and Engineering, Santana Dharma University, Paingan, Sleman, Yogyakarta, Indonesia
| | - Ganesh R Naik
- Faculty of Engineering and Information Technology, University of Technology Sydney, New South Wales, Australia
| | - Moran Gilat
- Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, New South Wales, Australia
| | - James M Shine
- Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, New South Wales, Australia
| | - Tuan N Nguyen
- Faculty of Engineering and Information Technology, University of Technology Sydney, New South Wales, Australia
| | - Quynh T Ly
- Faculty of Engineering and Information Technology, University of Technology Sydney, New South Wales, Australia
| | - Simon J G Lewis
- Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, New South Wales, Australia
| | - Hung T Nguyen
- Faculty of Engineering and Information Technology, University of Technology Sydney, New South Wales, Australia
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Naik GR, Al-Ani A, Gobbo M, Nguyen HT. Does Heel Height Cause Imbalance during Sit-to-Stand Task: Surface EMG Perspective. Front Physiol 2017; 8:626. [PMID: 28894422 PMCID: PMC5581500 DOI: 10.3389/fphys.2017.00626] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Accepted: 08/11/2017] [Indexed: 11/22/2022] Open
Abstract
The purpose of this study was to determine whether electromyography (EMG) muscle activities around the knee differ during sit-to-stand (STS) and returning task for females wearing shoes with different heel heights. Sixteen healthy young women (age = 25.2 ± 3.9 years, body mass index = 20.8 ± 2.7 kg/m2) participated in this study. Electromyography signals were recorded from the two muscles, vastus medialis (VM) and vastus lateralis (VL) that involve in the extension of knee. The participants wore shoes with five different heights, including 4, 6, 8, 10, and 12 cm. Surface electromyography (sEMG) data were acquired during STS and stand-to-sit-returning (STSR) tasks. The data was filtered using a fourth order Butterworth (band pass) filter of 20–450 Hz frequency range. For each heel height, we extracted median frequency (MDF) and root mean square (RMS) features to measure sEMG activities between VM and VL muscles. The experimental results (based on MDF and RMS-values) indicated that there is imbalance between vasti muscles for more elevated heels. The results are also quantified with statistical measures. The study findings suggest that there would be an increased likelihood of knee imbalance and fatigue with regular usage of high heel shoes (HHS) in women.
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Affiliation(s)
- Ganesh R Naik
- Centre for Health Technologies, Faculty of Engineering and IT, University of Technology SydneySydney, NSW, Australia.,Biomedical Engineering and Neuroscience Research Group, The MARCS Institute for Brain, Behaviour and Development, Western Sydney UniversityKingswood, NSW, Australia
| | - Ahmed Al-Ani
- Centre for Health Technologies, Faculty of Engineering and IT, University of Technology SydneySydney, NSW, Australia
| | - Massimiliano Gobbo
- Department of Clinical and Experimental Sciences, University of BresciaBrescia, Italy
| | - Hung T Nguyen
- Centre for Health Technologies, Faculty of Engineering and IT, University of Technology SydneySydney, NSW, Australia
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Tran Y, Naik GR, Nguyen TN, Craig A, Nguyen HT. Classification of EEG based-mental fatigue using principal component analysis and Bayesian neural network. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2016:4654-4657. [PMID: 28269312 DOI: 10.1109/embc.2016.7591765] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper presents an electroencephalography (EEG) based-classification of between pre- and post-mental load tasks for mental fatigue detection from 65 healthy participants. During the data collection, eye closed and eye open tasks were collected before and after conducting the mental load tasks. For the computational intelligence, the system uses the combination of principal component analysis (PCA) as the dimension reduction method of the original 26 channels of EEG data, power spectral density (PSD) as feature extractor and Bayesian neural network (BNN) as classifier. After applying the PCA, the dimension of the data is reduced from 26 EEG channels in 6 principal components (PCs) with above 90% of information retained. Based on this reduced dimension of 6 PCs of data, during eyes open, the classification pre-task (alert) vs. post-task (fatigue) using Bayesian neural network resulted in sensitivity of 76.8 %, specificity of 75.1% and accuracy of 76% Also based on data from the 6 PCs, during eye closed, the classification between pre- and post-task resulted in a sensitivity of 76.1%, specificity of 74.5% and accuracy of 75.3%. Further, the classification results of using only 6 PCs data are comparable to the result using the original 26 EEG channels. This finding will help in reducing the computational complexity of data analysis based on 26 channels of EEG for mental fatigue detection.
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Panwar M, Dyuthi SR, Chandra Prakash K, Biswas D, Acharyya A, Maharatna K, Gautam A, Naik GR. CNN based approach for activity recognition using a wrist-worn accelerometer. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:2438-2441. [PMID: 29060391 DOI: 10.1109/embc.2017.8037349] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.
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Naik GR, Pratihast M, Al-Ani A, Acharyya A, Nguyen HT. Differences in lower limb muscle activation patterns during Sit to Stand Task for different heel heights. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:2486-2489. [PMID: 29060403 DOI: 10.1109/embc.2017.8037361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
The purpose of this study was to investigate differences in lower limb muscle activation patterns for females wearing shoes with different heel heights during Sit to Stand Task (STS). Ten female participants with no prior history of neurological disorders participated in this study. Surface electromyography (sEMG) characteristics were recorded for four different heel heights (ranging from 4cm to 10cm) while performing the STS task. Signal processing analysis suggests that muscle activities increases on elevated heel heights, which may induce muscle imbalance for frequent STS tasks. In addition, results of muscle utilisation (percentage) for different heel heights suggest that lower limb muscles tend to compensate in order to maintain postural balance.
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Stephenson RM, Naik GR. A system for accelerometer-based gesture classification using artificial neural networks. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:4187-4190. [PMID: 29060820 DOI: 10.1109/embc.2017.8037779] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
A great many people suffer from neurological movement disorders that render typical hardware interface devices ineffective. A need exists for a universal interface device that can be trained to accept a wide range of inputs across varying types and severities of movement disorders. In this regard, this paper details the design, testing and optimization of an accelerometer-based gesture identification system. A Bluetooth-enabled IMU mounted on the wrist provides hand motion trajectory information to a local terminal. Several techniques are applied to decrease the intra-class variance and reduce classifier complexity including filtering, segmentation and temporal scaling. Datasets consisted of 520 training samples, 260 validation samples and a further 520 testing samples. A multi-layer feed forward artificial neural network (ML-FFNN) was used to classify the input space into 26 different classes. Initial system accuracy, using arbitrary hyperparameters was 77.69% with final optimized accuracy at 99.42%.
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Gautam A, Balouria A, Acharyya A, Acharyya SG, Panwar M, Naik GR. Shape memory effect of nano-ferromagnetic particle doped NiTi for orthopedic devices and rehabilitation techniques. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:950-953. [PMID: 29060030 DOI: 10.1109/embc.2017.8036982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper introduces a novel shape memory alloy (SMA) material for the controllability in the shape recovery of traditional SMA for orthopedic devices and rehabilitation techniques. The proposed material is formed by doping nano-ferromagnetic particle into porous NiTi alloy. The finite element analysis of shape memory effect property of the different distribution of nano-ferromagnetic particle is done and compared for same load and boundary conditions. The comparative analysis of the percentage change in volume deformation when load is released (for 2nd step) shows an average of 2.55 % with standard deviation of 1.69 whereas on thermal loading (for 3rd step) shows an average of 94.94% with standard deviation of 7.75 for all heterogeneous distribution of nano-particles in porous NiTi alloy. Our findings are, all the different conditions of heterogeneous distributions of nano-ferromagnetic particle doped NiTi alloy exhibits its inherent SME property.
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Naik GR, Tran Y, Craig A, Nguyen HT. Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:1808-1811. [PMID: 29060240 DOI: 10.1109/embc.2017.8037196] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.
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Ganesan B, Luximon A, Al-Jumaily A, Balasankar SK, Naik GR. Ponseti method in the management of clubfoot under 2 years of age: A systematic review. PLoS One 2017; 12:e0178299. [PMID: 28632733 PMCID: PMC5478104 DOI: 10.1371/journal.pone.0178299] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Accepted: 05/10/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Congenital talipes equinovarus (CTEV), also known as clubfoot, is common congenital orthopedic foot deformity in children characterized by four components of foot deformities: hindfoot equinus, hindfoot varus, midfoot cavus, and forefoot adduction. Although a number of conservative and surgical methods have been proposed to correct the clubfoot deformity, the relapses of the clubfoot are not uncommon. Several previous literatures discussed about the technical details of Ponseti method, adherence of Ponseti protocol among walking age or older children. However there is a necessity to investigate the relapse pattern, compliance of bracing, number of casts used in treatment and the percentages of surgical referral under two years of age for clear understanding and better practice to achieve successful outcome without or reduce relapse. Therefore this study aims to review the current evidence of Ponseti method (manipulation, casting, percutaneous Achilles tenotomy, and bracing) in the management of clubfoot under two years of age. MATERIALS AND METHODS Articles were searched from 2000 to 2015, in the following databases to identify the effectiveness of Ponseti method treatment for clubfoot: Medline, Cumulative Index to Nursing and Allied Health Literature (CINHAL), PubMed, and Scopus. The database searches were limited to articles published in English, and articles were focused on the effectiveness of Ponseti method on children with less than 2 years of age. RESULTS Of the outcome of 1095 articles from four electronic databases, twelve articles were included in the review. Pirani scoring system, Dimeglio scoring system, measuring the range of motion and rate of relapses were used as outcome measures. CONCLUSIONS In conclusion, all reviewed, 12 articles reported that Ponseti method is a very effective method to correct the clubfoot deformities. However, we noticed that relapses occur in nine studies, which is due to the non-adherence of bracing regime and other factors such as low income and social economic status.
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Affiliation(s)
- Balasankar Ganesan
- The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR
- Centre for Health Technology (CHT), Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, Sydney, Australia
| | | | - Adel Al-Jumaily
- Centre for Health Technology (CHT), Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, Sydney, Australia
| | | | - Ganesh R. Naik
- Centre for Health Technology (CHT), Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, Sydney, Australia
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Chai R, Naik GR, Nguyen TN, Ling SH, Tran Y, Craig A, Nguyen HT. Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System. IEEE J Biomed Health Inform 2017; 21:715-724. [DOI: 10.1109/jbhi.2016.2532354] [Citation(s) in RCA: 167] [Impact Index Per Article: 23.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Naik GR, Pendharkar G, Nguyen HT. Wavelet PCA for automatic identification of walking with and without an exoskeleton on a treadmill using pressure and accelerometer sensors. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2016:1999-2002. [PMID: 28268722 DOI: 10.1109/embc.2016.7591117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Nowadays portable devices with more number of sensors are used for gait assessment and monitoring for elderly and disabled. However, the problem with using multiple sensors is that if they are placed on the same platform or base, there could be cross talk between them, which could change the signal amplitude or add noise to the signal. Hence, this study uses wavelet PCA as a signal processing technique to separate the original sensor signal from the signal obtained from the sensors through the integrated unit to compare the two types of walking (with and without an exoskeleton). This comparison using wavelet PCA will enable the researchers to obtain accurate sensor data and compare and analyze the data in order to further improve the design of compact portable devices used to monitor and assess the gait in stroke or paralyzed subjects. The advantage of designing such systems is that they can also be used to assess and monitor the gait of the stroke subjects at home, which will save them time and efforts to visit the laboratory or clinic.
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Chai R, Ling SH, San PP, Naik GR, Nguyen TN, Tran Y, Craig A, Nguyen HT. Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks. Front Neurosci 2017; 11:103. [PMID: 28326009 PMCID: PMC5339284 DOI: 10.3389/fnins.2017.00103] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 02/17/2017] [Indexed: 11/13/2022] Open
Abstract
This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.
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Affiliation(s)
- Rifai Chai
- Faculty of Engineering and Information Technology, Centre for Health Technologies, University of Technology Sydney, NSW, Australia
| | - Sai Ho Ling
- Faculty of Engineering and Information Technology, Centre for Health Technologies, University of Technology Sydney, NSW, Australia
| | - Phyo Phyo San
- Data Analytic Department, Institute for Infocomm Research ASTAR, Singapore, Singapore
| | - Ganesh R Naik
- Faculty of Engineering and Information Technology, Centre for Health Technologies, University of Technology Sydney, NSW, Australia
| | - Tuan N Nguyen
- Faculty of Engineering and Information Technology, Centre for Health Technologies, University of Technology Sydney, NSW, Australia
| | - Yvonne Tran
- Faculty of Engineering and Information Technology, Centre for Health Technologies, University of TechnologySydney, NSW, Australia; Kolling Institute of Medical Research, Sydney Medical School, The University of SydneySydney, NSW, Australia
| | - Ashley Craig
- Kolling Institute of Medical Research, Sydney Medical School, The University of Sydney Sydney, NSW, Australia
| | - Hung T Nguyen
- Faculty of Engineering and Information Technology, Centre for Health Technologies, University of Technology Sydney, NSW, Australia
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Chai R, Naik GR, Ling SH, Nguyen HT. Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems. Biomed Eng Online 2017; 16:5. [PMID: 28086889 PMCID: PMC5234249 DOI: 10.1186/s12938-016-0303-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 12/19/2016] [Indexed: 11/25/2022] Open
Abstract
Background One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Methods This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Results Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.
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Affiliation(s)
- Rifai Chai
- Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia.
| | - Ganesh R Naik
- Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia
| | - Sai Ho Ling
- Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia
| | - Hung T Nguyen
- Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia
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Mopuri S, Reddy PS, Acharyya A, Naik GR. Fast underdetermined BSS architecture design methodology for real time applications. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:5408-11. [PMID: 26737514 DOI: 10.1109/embc.2015.7319614] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this paper, we propose a high speed architecture design methodology for the Under-determined Blind Source Separation (UBSS) algorithm using our recently proposed high speed Discrete Hilbert Transform (DHT) targeting real time applications. In UBSS algorithm, unlike the typical BSS, the number of sensors are less than the number of the sources, which is of more interest in the real time applications. The DHT architecture has been implemented based on sub matrix multiplication method to compute M point DHT, which uses N point architecture recursively and where M is an integer multiples of N. The DHT architecture and state of the art architecture are coded in VHDL for 16 bit word length and ASIC implementation is carried out using UMC 90 - nm technology @V DD = 1V and @ 1MHZ clock frequency. The proposed architecture implementation and experimental comparison results show that the DHT design is two times faster than state of the art architecture.
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Bhardwaj S, Jadhav P, Adapa B, Acharyya A, Naik GR. Online and automated reliable system design to remove blink and muscle artefact in EEG. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:6784-7. [PMID: 26737851 DOI: 10.1109/embc.2015.7319951] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electroencephalograms (EEGs) are progressively emerging as a significant measure of brain activity and are very effective tool for the diagnosis and treatment of mental and brain diseases and disorders including sleep apnea, Alzheimer's disease and Neurodevelopmental disorders. However, EEG signal is mixed with other biological signals including Ocular and Muscular artefacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners resulting less accurate diagnosis. In this paper we propose a real-time low-complexity and reliable system design methodology to remove these artefacts and noise in an automated fashion to aid online diagnosis under the pervasive personalized healthcare set-up without the need of any reference electrode. The simulation and hardware performance of the proposed methodology are measured and compared in terms of correlation and regression statistics lying above 80% and 67% which are much improved over the state-of-the art methodologies.
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Gautam A, Rani AB, Callejas MA, Acharyya SG, Acharyya A, Biswas D, Bhandari V, Sharma P, Naik GR. Shape memory alloy smart knee spacer to enhance knee functionality: model design and finite element analysis. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2016:6046-6049. [PMID: 28269631 DOI: 10.1109/embc.2016.7592107] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper we introduce Shape Memory Alloy (SMA) for designing the tibial part of Total Knee Arthroplasty (TKA) by exploiting the shape-memory and pseudo-elasticity property of the SMA (e.g. NiTi). This would eliminate the drawbacks of the state-of-the art PMMA based knee-spacer including fracture, sustainability, dislocation, tilting, translation and subluxation for tackling the Osteoarthritis especially for the aged people of 45-plus or the athletes. In this paper a Computer Aided Design (CAD) model using SolidWorks for the knee-spacer is presented based on the proposed SMA adopting the state-of-the art industry-standard geometry that is used in the PMMA based spacer design. Subsequently Ansys based Finite Element Analysis is carried out to measure and compare the performance between the proposed SMA based model with the state-of-the art PMMA ones. 81% more bending is noticed in the PMMA based spacer compared to the proposed SMA that would eventually cause fracture and tilting or translation of spacer. Permanent shape deformation of approximately 58.75% in PMMA based spacer is observed compared to recoverable 11% deformation in SMA when same load is applied on both separately.
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Gudur VY, Thallada S, Deevi AR, Gande VK, Acharyya A, Bhandari V, Sharma P, Khursheed S, Naik GR. Reconfigurable hardware-software codesign methodology for protein identification. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2016:2456-2459. [PMID: 28268821 DOI: 10.1109/embc.2016.7591227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper we propose an on-the-fly reconfigurable hardware-software codesign based reconfigurable solution for real-time protein identification. Reconfigurable string matching is performed in the disciplines of protein identification and biomarkers discovery. With the generation of plethora of sequenced data and number of biomarkers for several diseases, it is becoming necessary to have an accelerated processing and on-the-fly reconfigurable system design methodology to bring flexibility to its usage in the medical science community without the need of changing the entire hardware every time with the advent of new biomarker or protein. The proteome database of human at UniProtKB (Proteome ID up000005640) comprising of 42132 canonical and isoform proteins with variable database-size are used for testing the proposed design and the performance of the proposed system has been found to compare favorably with the state-of-the-art approaches with the additional advantage of real-time reconfigurability.
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Pendharkar G, Naik GR, Acharyya A, Nguyen HT. Multiscale PCA to distinguish regular and irregular surfaces using tri axial head and trunk acceleration signals. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:4122-5. [PMID: 26737201 DOI: 10.1109/embc.2015.7319301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This study uses multiscale principal component analysis (MSPCA) signal processing technique in order to distinguish the two different surfaces, tiled (regular) and cobbled (irregular) using accelerometry data (recorded from MTx sensors). Two MTx sensors were placed on the head and trunk of the subject while the subject walked freely over the regular and irregular surfaces during a free walk. 3D acceleration signals, vertical, medio lateral (ML) and anterior-posterior (AP) were recorded for the head and trunk segments and compared for the free walk on a defined route. The magnitude of the ML and AP acceleration obtained from the MTx sensors (for both head & trunk) was higher when walking over the irregular (cobbled) surface as compared to the regular (tiled) surface. The accelerometry data was initially analysed using MSPCA and was later classified using naïve Bayesian classifier with >86% accuracy. This research study demonstrates that MSPCA can be used to distinguish the regular and irregular surfaces. The proposed method could be very useful as an automated method for classification of the two surfaces.
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Vemishetty N, Jadhav P, Adapa B, Acharyya A, Pachamuthu R, Naik GR. Affordable low complexity heart/brain monitoring methodology for remote health care. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:5082-5. [PMID: 26737434 DOI: 10.1109/embc.2015.7319534] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper introduces a dual-mode low complex on-chip methodology for processing of ECG (Electrocardiogram) and EEG (Electroencephalography) signals, wherein based on the input switch the architecture can be dynamically configured to operate either as an ECG bio-marker or EEG signal de-noising system. In both the modes the signal processing technique depends on the output of the DWT (Discrete Wavelet Transform), hence a low complex methodology has been developed in which both ECG and EEG processing blocks sharing the same DWT block resulting in low area and low power consumption. The integrated ECG and EEG methodology has been implemented in Matlab, for verifying the ECG processing block the ECG database is taken from MIT-BIH PTBDB and IITH DB, similarly for EEG processing block the EEG signals are taken from PhysioNet database. The outcome of methodology in Matlab is equal to the results obtained from individual ECG and EEG blocks.
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Naik GR, Acharyya A, Nguyen HT. Classification of finger extension and flexion of EMG and Cyberglove data with modified ICA weight matrix. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2014:3829-32. [PMID: 25570826 DOI: 10.1109/embc.2014.6944458] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper reports the classification of finger flexion and extension of surface Electromyography (EMG) and Cyberglove data using the modified Independent Component Analysis (ICA) weight matrix. The finger flexion and extension data are processed through Principal Component Analysis (PCA), and next separated using modified ICA for each individual with customized weight matrix. The extension and flexion features of sEMG and Cyberglove (extracted from modified ICA) were classified using Linear Discriminant Analysis (LDA) with near 90% classification accuracy. The applications of this study include Human Computer Interface (HCI), virtual reality and neural prosthetics.
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Guo Y, Naik GR, Huang S, Abraham A, Nguyen HT. Nonlinear multiscale Maximal Lyapunov Exponent for accurate myoelectric signal classification. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.07.032] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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