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Ding C, Xiao R, Wang W, Holdsworth E, Hu X. Photoplethysmography based atrial fibrillation detection: a continually growing field. Physiol Meas 2024; 45:04TR01. [PMID: 38530307 DOI: 10.1088/1361-6579/ad37ee] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 03/26/2024] [Indexed: 03/27/2024]
Abstract
Objective. Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field.Approach. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies.Significance. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.
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Affiliation(s)
- Cheng Ding
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States of America
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Ran Xiao
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States of America
| | - Weijia Wang
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States of America
| | - Elizabeth Holdsworth
- Georgia Tech Library, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States of America
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States of America
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States of America
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Jeanningros L, Le Bloa M, Teres C, Herrera Siklody C, Porretta A, Pascale P, Luca A, Solana Muñoz J, Domenichini G, Meister TA, Soria Maldonado R, Tanner H, Vesin JM, Thiran JP, Lemay M, Rexhaj E, Pruvot E, Braun F. The influence of cardiac arrhythmias on the detection of heartbeats in the photoplethysmogram: benchmarking open-source algorithms. Physiol Meas 2024; 45:025005. [PMID: 38266291 DOI: 10.1088/1361-6579/ad2216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 01/24/2024] [Indexed: 01/26/2024]
Abstract
Objective.Cardiac arrhythmias are a leading cause of mortality worldwide. Wearable devices based on photoplethysmography give the opportunity to screen large populations, hence allowing for an earlier detection of pathological rhythms that might reduce the risks of complications and medical costs. While most of beat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation recordings, the performance of these algorithms in patients with other cardiac arrhythmias, such as ventricular tachycardia or bigeminy, remain unknown to date.Approach. ThePPG-beatsopen-source framework, developed by Charlton and colleagues, evaluates the performance of the beat detectors namedQPPG,MSPTDandABDamong others. We applied thePPG-beatsframework on two newly acquired datasets, one containing seven different types of cardiac arrhythmia in hospital settings, and another dataset including two cardiac arrhythmias in ambulatory settings.Main Results. In a clinical setting, theQPPGbeat detector performed best on atrial fibrillation (with a medianF1score of 94.4%), atrial flutter (95.2%), atrial tachycardia (87.0%), sinus rhythm (97.7%), ventricular tachycardia (83.9%) and was ranked 2nd for bigeminy (75.7%) behindABDdetector (76.1%). In an ambulatory setting, theMSPTDbeat detector performed best on normal sinus rhythm (94.6%), and theQPPGdetector on atrial fibrillation (91.6%) and bigeminy (80.0%).Significance. Overall, the PPG beat detectorsQPPG,MSPTDandABDconsistently achieved higher performances than other detectors. However, the detection of beats from wrist-PPG signals is compromised in presence of bigeminy or ventricular tachycardia.
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Affiliation(s)
- Loïc Jeanningros
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
- Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
| | - Mathieu Le Bloa
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Cheryl Teres
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | | | | | - Patrizio Pascale
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Adrian Luca
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Jorge Solana Muñoz
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Giulia Domenichini
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Théo A Meister
- Department of Cardiology and Biomedical Research, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Rodrigo Soria Maldonado
- Department of Cardiology and Biomedical Research, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Hildegard Tanner
- Department of Cardiology and Biomedical Research, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Jean-Marc Vesin
- Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
| | | | - Mathieu Lemay
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
| | - Emrush Rexhaj
- Department of Cardiology and Biomedical Research, University Hospital Bern, University of Bern, Bern, Switzerland
| | - Etienne Pruvot
- Service of Cardiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Fabian Braun
- Swiss Center for Electronics and Microtechnology, Neuchâtel, Switzerland
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彭 仲, 崔 兴, 张 政, 俞 梦. [Wearable devices: Perspectives on assessing and monitoring human physiological status]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:1045-1052. [PMID: 38151926 PMCID: PMC10753302 DOI: 10.7507/1001-5515.202303043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 08/28/2023] [Indexed: 12/29/2023]
Abstract
This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis-dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.
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Affiliation(s)
- 仲康 彭
- 东南大学 生物科学与医学工程学院(南京 210096)School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 非线性动态医学研究中心(南京 210096)Center for Nonlinear Dynamics in Medicine, Southeast University, Nanjing 210096, P. R. China
- 哈佛大学 医学院/贝斯以色列女执事医疗中心(美国 波士顿 02215)Beth Israel Deaconess Medical Center / Harvard Medical School, Boston 02215, USA
| | - 兴然 崔
- 东南大学 生物科学与医学工程学院(南京 210096)School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, P. R. China
- 东南大学 非线性动态医学研究中心(南京 210096)Center for Nonlinear Dynamics in Medicine, Southeast University, Nanjing 210096, P. R. China
| | - 政波 张
- 东南大学 生物科学与医学工程学院(南京 210096)School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, P. R. China
| | - 梦孙 俞
- 东南大学 生物科学与医学工程学院(南京 210096)School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, P. R. China
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Talukdar D, De Deus LF, Sehgal N. Evaluation of Atrial Fibrillation Detection in Short-Term Photoplethysmography (PPG) Signals Using Artificial Intelligence. Cureus 2023; 15:e45111. [PMID: 37842400 PMCID: PMC10568237 DOI: 10.7759/cureus.45111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
Background Atrial fibrillation (AFIB) is a common atrial arrhythmia that affects millions of people worldwide. However, most of the time, AFIB is paroxysmal and can pass unnoticed in medical exams; therefore, regular screening is required. This paper proposes machine learning (ML) methods to detect AFIB from short-term electrocardiogram (ECG) and photoplethysmography (PPG) signals. Aim Several experiments were conducted across five different databases, with three of them containing ECG signals and the other two consisting of only PPG signals. Experiments were conducted to investigate the hypothesis that an ML model trained to predict AFIB from ECG segments could be used to predict AFIB from PPG segments. Materials and methods A random forest (RF) ML algorithm achieved the best accuracy and achieved a 90% accuracy rate on the University of Mississippi Medical Center (UMMC) dataset (216 samples) and a 97% accuracy rate on the Medical Information Mart for Intensive Care (MIMIC)-III datasets (2,134 samples). Results A total of 269,842 signal segments were analyzed across all datasets (212,266 were of normal sinus rhythm (NSR) and 57,576 corresponded to AFIB segments). Conclusions The ability to detect AFIB with significant accuracy using ML algorithms from PPG signals, which can be acquired via non-invasive contact or contactless, is a promising step forward toward the goal of achieving large-scale screening for AFIB.
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Affiliation(s)
- Debjyoti Talukdar
- Medical Research, Mkhitar Gosh Armenian-Russian International University, Yerevan, ARM
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Tseng AS, Kowlgi GN, DeSimone CV. Management of Premature Ventricular Complexes in the Outpatient Setting. Mayo Clin Proc 2023; 98:1042-1053. [PMID: 37419573 DOI: 10.1016/j.mayocp.2023.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/18/2023] [Accepted: 01/30/2023] [Indexed: 07/09/2023]
Abstract
With an aging population and an explosion in ambulatory electrocardiographic monitoring, primary care clinicians will undoubtedly see an increase in patients presenting with premature ventricular complexes (PVCs), especially given how common they are in the general population. A sizable number of patients with PVCs are asymptomatic, and these PVCs have no significant clinical implications. In contrast, PVCs can predispose patients to or are a clinical marker of an underlying cardiomyopathy, heart failure, or sudden cardiac death. Such a dichotomy can create fear in dealing with PVCs in the outpatient setting, both in immediate situations and for surveillance and follow-up. In this review, we provide a comprehensive overview of the pathophysiologic basis of PVCs, appropriate diagnostic testing, management, and prognostic considerations necessary when faced with PVCs in the outpatient clinic. We also provide a simplistic approach to help guide the initial work-up of PVCs, basic treatment strategies, and indications of when to refer for cardiovascular specialty consultation to improve physician comfort and to enhance patient care.
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Affiliation(s)
- Andrew S Tseng
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN
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6
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Wrist photoplethysmography-based assessment of ectopic burden in hemodialysis patients. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
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Tran KV, Filippaios A, Noorishirazi K, Ding E, Han D, Mohagheghian F, Dai Q, Mehawej J, Wang Z, Lessard D, Otabil EM, Hamel A, Paul T, Gottbrecht MF, Fitzgibbons TP, Saczynski J, Chon KH, McManus DD. False Atrial Fibrillation Alerts from Smartwatches are Associated with Decreased Perceived Physical Well-being and Confidence in Chronic Symptoms Management. CARDIOLOGY AND CARDIOVASCULAR MEDICINE 2023; 7:97-107. [PMID: 37476150 PMCID: PMC10358285 DOI: 10.26502/fccm.92920314] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Wrist-based wearables have been FDA approved for AF detection. However, the health behavior impact of false AF alerts from wearables on older patients at high risk for AF are not known. In this work, we analyzed data from the Pulsewatch (NCT03761394) study, which randomized patients (≥50 years) with history of stroke or transient ischemic attack to wear a patch monitor and a smartwatch linked to a smartphone running the Pulsewatch application vs to only the cardiac patch monitor over 14 days. At baseline and 14 days, participants completed validated instruments to assess for anxiety, patient activation, perceived mental and physical health, chronic symptom management self-efficacy, and medicine adherence. We employed linear regression to examine associations between false AF alerts with change in patient-reported outcomes. Receipt of false AF alerts was related to a dose-dependent decline in self-perceived physical health and levels of disease self-management. We developed a novel convolutional denoising autoencoder (CDA) to remove motion and noise artifacts in photoplethysmography (PPG) segments to optimize AF detection, which substantially reduced the number of false alerts. A promising approach to avoid negative impact of false alerts is to employ artificial intelligence driven algorithms to improve accuracy.
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Affiliation(s)
- Khanh-Van Tran
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Andreas Filippaios
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Kamran Noorishirazi
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Eric Ding
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Dong Han
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Storrs, CT 06269, USA
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Storrs, CT 06269, USA
| | - Qiying Dai
- Division of Cardiovascular Medicine, Department of Medicine, Saint Vincent Hospital, 123 Summer Street, Worcester, MA 01608, USA
| | - Jordy Mehawej
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Ziyue Wang
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Darleen Lessard
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Edith Mensah Otabil
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Alex Hamel
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Tenes Paul
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Matthew F Gottbrecht
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Timothy P Fitzgibbons
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
| | - Jane Saczynski
- Department of Pharmacy and Health Systems Sciences, Northeastern University, Boston, Massachusetts, USA
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, 260 Glenbrook Road, Storrs, CT 06269, USA
| | - David D McManus
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts, Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
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Leung T, Ding EY, Cho C, Jung H, Dickson EL, Mohagheghian F, Peitzsch AG, DiMezza D, Tran KV, McManus DD, Chon KH. A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older Adults After Stroke or Transient Ischemic Attack: Application Design Study. JMIR Cardio 2023; 7:e41691. [PMID: 36780211 PMCID: PMC9972205 DOI: 10.2196/41691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/21/2022] [Accepted: 12/31/2022] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND The prevalence of atrial fibrillation (AF) increases with age and can lead to stroke. Therefore, older adults may benefit the most from AF screening. However, older adult populations tend to lag more than younger groups in the adoption of, and comfort with, the use of mobile health (mHealth) apps. Furthermore, although mobile apps that can detect AF are available to the public, most are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle large data collections, especially in this age group. OBJECTIVE This study aimed to design an innovative smartwatch-based AF monitoring mHealth solution in collaboration with older adult participants and clinicians. METHODS The Pulsewatch system is designed to link smartwatches and smartphone apps, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system is designed to continuously monitor the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system is designed to serve as the data-transferring hub to the cloud storage server. RESULTS We implemented the Pulsewatch system based on the functionality that patients and caregivers recommended. The user interfaces of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on feedback from focus groups consisting of patients with stroke and clinicians. The Pulsewatch system was used by the intervention group for up to 6 weeks in the 2 phases of our randomized clinical trial. At the conclusion of phase 1, 90 trial participants who had used the Pulsewatch app and smartwatch for 14 days completed a System Usability Scale to assess the usability of the Pulsewatch system; of 88 participants, 56 (64%) endorsed that the smartwatch app is "easy to use." For phases 1 and 2 of the study, we collected 9224.4 hours of smartwatch recordings from the participants. The longest recording streak in phase 2 was 21 days of consecutive recordings out of the 30 days of data collection. CONCLUSIONS This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In this paper, we report on the system's usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment. TRIAL REGISTRATION ClinicalTrials.gov NCT03761394; https://www.clinicaltrials.gov/ct2/show/NCT03761394. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1016/j.cvdhj.2021.07.002.
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Affiliation(s)
| | - Eric Y Ding
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Chaeho Cho
- Zebra Technologies Inc, Holtsville, NY, United States
| | - Haewook Jung
- SSP, Seoul, Republic of Korea.,Mediporte Co, Ltd, Gyeonggi-do, Republic of Korea
| | - Emily L Dickson
- College of Osteopathic Medicine, Des Moines University, Des Moines, IA, United States
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Andrew G Peitzsch
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | | | - Khanh-Van Tran
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - David D McManus
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
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Okamoto M, Murao K. PPG2EMG: Estimating Upper-Arm Muscle Activities and EMG from Wrist PPG Values. SENSORS (BASEL, SWITZERLAND) 2023; 23:1782. [PMID: 36850382 PMCID: PMC9962560 DOI: 10.3390/s23041782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 01/28/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
The electromyogram (EMG) is a waveform representation of the action potential generated by muscle cells using electrodes. EMG acquired using surface electrodes is called surface EMG (sEMG), and it is the acquisition of muscle action potentials transmitted by volume conduction from the skin. Surface electrodes require disposable conductive gel or adhesive tape to be attached to the skin, which is costly to run, and the tape is hard on the skin when it is removed. Muscle activity can be evaluated by acquiring muscle potentials and analyzing quantitative, temporal, and frequency factors. It is also possible to evaluate muscle fatigue because the frequency of the EMG becomes lower as the muscle becomes fatigued. Research on human activity recognition from EMG signals has been actively conducted and applied to systems that support arm and hand functions. This paper proposes a method for recognizing the muscle activity state of the arm using pulse wave data (PPG: Photoplethysmography) and a method for estimating EMG using pulse wave data. This paper assumes that the PPG sensor is worn on the user's wrist to measure the heart rate. The user also attaches an elastic band to the upper arm, and when the user exerts a force on the arm, the muscles of the upper arm contract. The arteries are then constricted, and the pulse wave measured at the wrist becomes weak. From the change in the pulse wave, the muscle activity of the arm can be recognized and the number of action potentials of the muscle can be estimated. From the evaluation experiment with five subjects, three types of muscle activity were recognized with 80+%, and EMG was estimated with approximately 20% error rate.
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Kudo S, Chen Z, Zhou X, Izu LT, Chen-Izu Y, Zhu X, Tamura T, Kanaya S, Huang M. A training pipeline of an arrhythmia classifier for atrial fibrillation detection using Photoplethysmography signal. Front Physiol 2023; 14:1084837. [PMID: 36744032 PMCID: PMC9892629 DOI: 10.3389/fphys.2023.1084837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/02/2023] [Indexed: 01/20/2023] Open
Abstract
Photoplethysmography (PPG) signal is potentially suitable in atrial fibrillation (AF) detection for its convenience in use and similarity in physiological origin to electrocardiogram (ECG). There are a few preceding studies that have shown the possibility of using the peak-to-peak interval of the PPG signal (PPIp) in AF detection. However, as a generalized model, the accuracy of an AF detector should be pursued on the one hand; on the other hand, its generalizability should be paid attention to in view of the individual differences in PPG manifestation of even the same arrhythmia and the existence of sub-types. Moreover, a binary classifier for atrial fibrillation and normal sinus rhythm is not convincing enough for the similarity between AF and ectopic beats. In this study, we project the atrial fibrillation detection as a multiple-class classification and try to propose a training pipeline that is advantageous both to the accuracy and generalizability of the classifier by designing and determining the configurable options of the pipeline, in terms of input format, deep learning model (with hyperparameter optimization), and scheme of transfer learning. With a rigorous comparison of the possible combinations of the configurable components in the pipeline, we confirmed that first-order difference of heartbeat sequence as the input format, a 2-layer CNN-1-layer Transformer hybridR model as the learning model and the whole model fine-tuning as the implementing scheme of transfer learning is the best combination for the pipeline (F1 value: 0.80, overall accuracy: 0.87)R.
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Affiliation(s)
- Sota Kudo
- Computational Systems Biology Lab, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | | | - Xue Zhou
- Computational Systems Biology Lab, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Leighton T. Izu
- Department of Pharmacology, University of California, Davis, Davis, CA, United States
| | - Ye Chen-Izu
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, United States
| | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Aizu-Wakamatsu, Japan
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo, Japan
| | - Shigehiko Kanaya
- Computational Systems Biology Lab, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan
| | - Ming Huang
- Computational Systems Biology Lab, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan,*Correspondence: Ming Huang ,
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Ullah H, Heyat MBB, Akhtar F, Muaad AY, Ukwuoma CC, Bilal M, Miraz MH, Bhuiyan MAS, Wu K, Damaševičius R, Pan T, Gao M, Lin Y, Lai D. An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal. Diagnostics (Basel) 2022; 13:diagnostics13010087. [PMID: 36611379 PMCID: PMC9818233 DOI: 10.3390/diagnostics13010087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/05/2022] [Accepted: 12/23/2022] [Indexed: 12/29/2022] Open
Abstract
The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan-Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.
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Affiliation(s)
- Hadaate Ullah
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | | | - Chiagoziem C. Ukwuoma
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Muhammad Bilal
- College of Pharmacy, Liaquat University of Medical and Health Sciences, Jamshoro 76090, Pakistan
| | - Mahdi H. Miraz
- School of Computing and Data Science, Xiamen University Malaysia, Bandar Sunsuria, Sepang 43900, Malaysia
- School of Computing, Glyndŵr University, Wrexham LL11 2AW, UK
| | | | - Kaishun Wu
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| | - Robertas Damaševičius
- Department of Software Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Taisong Pan
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Min Gao
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yuan Lin
- State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Materials and Energy, University of Electronic Science and Technology of China, Chengdu 610054, China
- Medico-Engineering Corporation on Applied Medicine Research Center, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
| | - Dakun Lai
- Biomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
- Correspondence: (M.B.B.H.); (R.D.); (Y.L.); (D.L.)
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Mohagheghian F, Han D, Peitzsch A, Nishita N, Ding E, Dickson EL, DiMezza D, Otabil EM, Noorishirazi K, Scott J, Lessard D, Wang Z, Whitcomb C, Tran KV, Fitzgibbons TP, McManus DD, Chon KH. Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection. IEEE Trans Biomed Eng 2022; 69:2982-2993. [PMID: 35275809 PMCID: PMC9478959 DOI: 10.1109/tbme.2022.3158582] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals. METHODS We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments. CONCLUSION A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work. SIGNIFICANCE As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.
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Asiain D, Ponce de León J, Beltrán JR. MsWH: A Multi-Sensory Hardware Platform for Capturing and Analyzing Physiological Emotional Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:5775. [PMID: 35957330 PMCID: PMC9371105 DOI: 10.3390/s22155775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/22/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
This paper presents a new physiological signal acquisition multi-sensory platform for emotion detection: Multi-sensor Wearable Headband (MsWH). The system is capable of recording and analyzing five different physiological signals: skin temperature, blood oxygen saturation, heart rate (and its variation), movement/position of the user (more specifically of his/her head) and electrodermal activity/bioimpedance. The measurement system is complemented by a porthole camera positioned in such a way that the viewing area remains constant. Thus, the user's face will remain centered regardless of its position and movement, increasing the accuracy of facial expression recognition algorithms. This work specifies the technical characteristics of the developed device, paying special attention to both the hardware used (sensors, conditioning, microprocessors, connections) and the software, which is optimized for accurate and massive data acquisition. Although the information can be partially processed inside the device itself, the system is capable of sending information via Wi-Fi, with a very high data transfer rate, in case external processing is required. The most important features of the developed platform have been compared with those of a proven wearable device, namely the Empatica E4 wristband, in those measurements in which this is possible.
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Affiliation(s)
- David Asiain
- Department of Electronics, Escuela Universitaria Politécnica de La Almunia, La Almunia de Doña Godina, 50100 Zaragoza, Spain;
| | - Jesús Ponce de León
- Department of Electronics, Escuela Universitaria Politécnica de La Almunia, La Almunia de Doña Godina, 50100 Zaragoza, Spain;
| | - José Ramón Beltrán
- Department of Electronics Engineering and Communications, Escuela de Ingeniería y Arquitectura, I3A, Universidad de Zaragoza, 50018 Zaragoza, Spain;
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Sološenko A, Paliakaitė B, Marozas V, Sörnmo L. Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection. Front Physiol 2022; 13:928098. [PMID: 35923223 PMCID: PMC9339964 DOI: 10.3389/fphys.2022.928098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/15/2022] [Indexed: 11/23/2022] Open
Abstract
Objective: To develop a method for detection of bradycardia and ventricular tachycardia using the photoplethysmogram (PPG). Approach: The detector is based on a dual-branch convolutional neural network (CNN), whose input is the scalograms of the continuous wavelet transform computed in 5-s segments. Training and validation of the CNN is accomplished using simulated PPG signals generated from RR interval series extracted from public ECG databases. Manually annotated real PPG signals from the PhysioNet/CinC 2015 Challenge Database are used for performance evaluation. The performance is compared to that of a pulse-based reference detector. Results: The sensitivity/specificity were found to be 98.1%/97.9 and 76.6%/96.8% for the CNN-based detector, respectively, whereas the corresponding results for the pulse-based detector were 94.7%/99.8 and 67.1%/93.8%, respectively. Significance: The proposed detector may be useful for continuous, long-term monitoring of bradycardia and tachycardia using wearable devices, e.g., wrist-worn devices, especially in situations where sensitivity is favored over specificity. The study demonstrates that simulated PPG signals are suitable for training and validation of a CNN.
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Affiliation(s)
- Andrius Sološenko
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
- *Correspondence: Andrius Sološenko ,
| | - Birutė Paliakaitė
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
| | - Vaidotas Marozas
- Biomedical Engineering Institute, Kaunas University of Technology, Kaunas, Lithuania
- Department of Electronics Engineering, Kaunas University of Technology, Kaunas, Lithuania
| | - Leif Sörnmo
- Department of Biomedical Engineering, Lund University, Lund, Sweden
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Sivanandarajah P, Wu H, Bajaj N, Khan S, Ng FS. Is machine learning the future for atrial fibrillation screening? CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:136-145. [PMID: 35720677 PMCID: PMC9204790 DOI: 10.1016/j.cvdhj.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.
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Affiliation(s)
- Pavidra Sivanandarajah
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Huiyi Wu
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Nikesh Bajaj
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Sadia Khan
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
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A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia. BIOSENSORS 2022; 12:bios12020082. [PMID: 35200342 PMCID: PMC8869811 DOI: 10.3390/bios12020082] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 01/25/2022] [Accepted: 01/27/2022] [Indexed: 01/20/2023]
Abstract
Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Methods: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung’s Gear S3 and Galaxy Watch 3. Results: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors—30% and 66% lower—and mean heart rate and mean interbeat interval estimation errors—60% and 77% lower—when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. Conclusion: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. Significance: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.
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Zhu L, Nathan V, Kuang J, Kim J, Avram R, Olgin J, Gao J. Atrial Fibrillation Detection and Atrial Fibrillation Burden Estimation via Wearables. IEEE J Biomed Health Inform 2021; 26:2063-2074. [PMID: 34855603 DOI: 10.1109/jbhi.2021.3131984] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Atrial Fibrillation (AF) is an important cardiac rhythm disorder, which if left untreated can lead to serious complications such as a stroke. AF can remain asymptomatic, and it can progressively worsen over time; it is thus a disorder that would benefit from detection and continuous monitoring with a wearable sensor. We develop an AF detection algorithm, deploy it on a smartwatch, and prospectively and comprehensively validate its performance on a real-world population that included patients diagnosed with AF. The algorithm showed a sensitivity of 87.8% and a specificity of 97.4% over every 5-minute segment of PPG evaluated. Furthermore, we introduce novel algorithm blocks and system designs to increase the time of coverage and monitor for AF even during periods of motion noise and other artifacts that would be encountered in daily-living scenarios. An average of 67.8% of the entire duration the patients wore the smartwatch produced a valid decision. Finally, we present the ability of our algorithm to function throughout the day and estimate the AF burden, a first-of-this-kind measure using a wearable sensor, showing 98% correlation with the ground truth and an average error of 6.2%.
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Ramesh J, Solatidehkordi Z, Aburukba R, Sagahyroon A. Atrial Fibrillation Classification with Smart Wearables Using Short-Term Heart Rate Variability and Deep Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2021; 21:7233. [PMID: 34770543 PMCID: PMC8587743 DOI: 10.3390/s21217233] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/23/2021] [Accepted: 10/26/2021] [Indexed: 02/04/2023]
Abstract
Atrial fibrillation (AF) is a type of cardiac arrhythmia affecting millions of people every year. This disease increases the likelihood of strokes, heart failure, and even death. While dedicated medical-grade electrocardiogram (ECG) devices can enable gold-standard analysis, these devices are expensive and require clinical settings. Recent advances in the capabilities of general-purpose smartphones and wearable technology equipped with photoplethysmography (PPG) sensors increase diagnostic accessibility for most populations. This work aims to develop a single model that can generalize AF classification across the modalities of ECG and PPG with a unified knowledge representation. This is enabled by approximating the transformation of signals obtained from low-cost wearable PPG sensors in terms of Pulse Rate Variability (PRV) to temporal Heart Rate Variability (HRV) features extracted from medical-grade ECG. This paper proposes a one-dimensional deep convolutional neural network that uses HRV-derived features for classifying 30-s heart rhythms as normal sinus rhythm or atrial fibrillation from both ECG and PPG-based sensors. The model is trained with three MIT-BIH ECG databases and is assessed on a dataset of unseen PPG signals acquired from wrist-worn wearable devices through transfer learning. The model achieved the aggregate binary classification performance measures of accuracy: 95.50%, sensitivity: 94.50%, and specificity: 96.00% across a five-fold cross-validation strategy on the ECG datasets. It also achieved 95.10% accuracy, 94.60% sensitivity, 95.20% specificity on an unseen PPG dataset. The results show considerable promise towards seamless adaptation of gold-standard ECG trained models for non-ambulatory AF detection with consumer wearable devices through HRV-based knowledge transfer.
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Affiliation(s)
| | | | - Raafat Aburukba
- Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates; (J.R.); (Z.S.); (A.S.)
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Nazarian S, Lam K, Darzi A, Ashrafian H. Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis. J Med Internet Res 2021; 23:e28974. [PMID: 34448706 PMCID: PMC8433941 DOI: 10.2196/28974] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 04/24/2021] [Accepted: 06/14/2021] [Indexed: 01/29/2023] Open
Abstract
Background Significant morbidity, mortality, and financial burden are associated with cardiac rhythm abnormalities. Conventional investigative tools are often unsuccessful in detecting cardiac arrhythmias because of their episodic nature. Smartwatches have gained popularity in recent years as a health tool for the detection of cardiac rhythms. Objective This study aims to systematically review and meta-analyze the diagnostic accuracy of smartwatches in the detection of cardiac arrhythmias. Methods A systematic literature search of the Embase, MEDLINE, and Cochrane Library databases was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies reporting the use of a smartwatch for the detection of cardiac arrhythmia. Summary estimates of sensitivity, specificity, and area under the curve were attempted using a bivariate model for the diagnostic meta-analysis. Studies were examined for quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Results A total of 18 studies examining atrial fibrillation detection, bradyarrhythmias and tachyarrhythmias, and premature contractions were analyzed, measuring diagnostic accuracy in 424,371 subjects in total. The signals analyzed by smartwatches were based on photoplethysmography. The overall sensitivity, specificity, and accuracy of smartwatches for detecting cardiac arrhythmias were 100% (95% CI 0.99-1.00), 95% (95% CI 0.93-0.97), and 97% (95% CI 0.96-0.99), respectively. The pooled positive predictive value and negative predictive value for detecting cardiac arrhythmias were 85% (95% CI 0.79-0.90) and 100% (95% CI 1.0-1.0), respectively. Conclusions This review demonstrates the evolving field of digital disease detection. The current diagnostic accuracy of smartwatch technology for the detection of cardiac arrhythmias is high. Although the innovative drive of digital devices in health care will continue to gain momentum toward screening, the process of accurate evidence accrual and regulatory standards ready to accept their introduction is strongly needed. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020213237; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=213237.
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Affiliation(s)
| | - Kyle Lam
- Imperial College London, London, United Kingdom
| | - Ara Darzi
- Imperial College London, London, United Kingdom
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