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Kim SG, Cho SW, Rhee CS, Kim JW. How to objectively measure snoring: a systematic review. Sleep Breath 2024; 28:1-9. [PMID: 37421520 DOI: 10.1007/s11325-023-02865-6] [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: 02/12/2023] [Revised: 05/18/2023] [Accepted: 05/31/2023] [Indexed: 07/10/2023]
Abstract
PURPOSE Snoring is the most common symptom of obstructive sleep apnea. Various objective methods of measuring snoring are available, and even if the measurement is performed the same way, communication is difficult because there are no common reference values between the researcher and clinician with regard to intensity and frequency, among other variables. In other words, no consensus regarding objective measurement has been reached. This study aimed to review the literature related to the objective measurement of snoring, such as measurement devices, definitions, and device locations. METHODS A literature search based on the PubMed, Cochrane, and Embase databases was conducted from the date of inception to April 5, 2023. Twenty-nine articles were included in this study. Articles that mentioned only the equipment used for measurement and did not include individual details were excluded from the study. RESULTS Three representative methods for measuring snoring emerged. These include (1) a microphone, which measures snoring sound; (2) piezoelectric sensor, which measures snoring vibration; and (3) nasal transducer, which measures airflow. In addition, recent attempts have been made to measure snoring using smartphones and applications. CONCLUSION Numerous studies have investigated both obstructive sleep apnea and snoring. However, the objective methods of measuring snoring and snoring-related concepts vary across studies. Consensus in the academic and clinical communities on how to measure and define snoring is required.
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Affiliation(s)
- Su Geun Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Sung-Woo Cho
- Department of Otorhinolaryngology‑Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 173‑82 Gumi‑ro, Bundang‑gu, Seongnam, Gyeonggi‑do, 13620, South Korea
| | - Chae-Seo Rhee
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
- Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul, Korea
| | - Jeong-Whun Kim
- Department of Otorhinolaryngology‑Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 173‑82 Gumi‑ro, Bundang‑gu, Seongnam, Gyeonggi‑do, 13620, South Korea.
- Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul, Korea.
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Duarte M, Pereira-Rodrigues P, Ferreira-Santos D. The Role of Novel Digital Clinical Tools in the Screening or Diagnosis of Obstructive Sleep Apnea: Systematic Review. J Med Internet Res 2023; 25:e47735. [PMID: 37494079 PMCID: PMC10413091 DOI: 10.2196/47735] [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: 03/31/2023] [Revised: 05/18/2023] [Accepted: 05/23/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard. OBJECTIVE This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population. METHODS We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures. RESULTS We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)≥30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI≥30. It uses the Sonomat-a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events. CONCLUSIONS These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings. TRIAL REGISTRATION PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748.
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Affiliation(s)
- Miguel Duarte
- Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pedro Pereira-Rodrigues
- Faculty of Medicine, University of Porto, Porto, Portugal
- Department of Community Medicine, Information and Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - Daniela Ferreira-Santos
- Faculty of Medicine, University of Porto, Porto, Portugal
- Department of Community Medicine, Information and Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
- Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Porto, Portugal
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Fedorovich AA, Gorshkov AY, Korolev AI, Drapkina OM. Smartphone in medicine — from a reference book to a diagnostic system. Overview of the current state of the issue. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2022. [DOI: 10.15829/1728-8800-2022-3298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The paper provides a brief overview of the modern possibilities of using a smartphone as a diagnostic device of a wide profile. In some cases, additional specialized attachments are required. In others, the diagnostic algorithm uses only standard cameras, a microphone and various built-in smartphone sensors. The development of the smartphone integration into the healthcare system is modern, relevant and very promising, given the widespread use of smartphones among the global population.
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Affiliation(s)
- A. A. Fedorovich
- National Medical Research Center for Therapy and Preventive Medicine;
Institute of Biomedical Problems
| | - A. Yu. Gorshkov
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. I. Korolev
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
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Isolated REM sleep behaviour disorder: current diagnostic procedures and emerging new technologies. J Neurol 2022; 269:4684-4695. [PMID: 35748910 PMCID: PMC9363360 DOI: 10.1007/s00415-022-11213-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/31/2022] [Accepted: 06/01/2022] [Indexed: 11/03/2022]
Abstract
Isolated REM sleep behaviour disorder (iRBD) is characterised by dream enactment behaviours, such as kicking and punching while asleep, and vivid/violent dreams. It is now acknowledged as a prodromal phase of neurodegenerative disease-approximately 80% of people with iRBD will develop dementia with Lewy Bodies, Parkinson's disease or another degenerative brain disease within 10 years. It is important that neurologists and other clinicians understand how to make an early accurate diagnosis of iRBD so that affected people can have the opportunity to take part in clinical trials. However, making a diagnosis can be clinically challenging due to a variety of reasons, including delayed referral, symptom overlap with other disorders, and uncertainty about how to confirm a diagnosis. Several methods of assessment are available, such as clinical interview, screening questionnaires and video polysomnography or 'sleep study'. This review aims to support clinical neurologists in assessing people who present with symptoms suggestive of iRBD. We describe the usefulness and limitations of each diagnostic method currently available in clinical practice, and present recent research on the utility of new wearable technologies to assist with iRBD diagnosis, which may offer a more practical assessment method for clinicians. This review highlights the importance of thorough clinical investigation when patients present with suspected iRBD and emphasises the need for easier access to diagnostic procedures for accurate and early diagnosis.
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Kim DH, Kim SW, Hwang SH. Diagnostic value of smartphone in obstructive sleep apnea syndrome: A systematic review and meta-analysis. PLoS One 2022; 17:e0268585. [PMID: 35587944 PMCID: PMC9119483 DOI: 10.1371/journal.pone.0268585] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 05/03/2022] [Indexed: 01/13/2023] Open
Abstract
Objectives To assess the diagnostic utility of smartphone-based measurement in detecting moderate to severe obstructive sleep apnea syndrome (OSAS). Methods Six databases were thoroughly reviewed. Random-effect models were used to estimate the summary sensitivity, specificity, negative predictive value, positive predictive value, diagnostic odds ratio, summary receiver operating characteristic curve and measured the areas under the curve. To assess the accuracy and precision, pooled mean difference and standard deviation of apnea hypopnea index (AHI) between smartphone and polysomnography (95% limits of agreement) across studies were calculated using the random-effects model. Study methodological quality was evaluated using the QUADAS-2 tool. Results Eleven studies were analyzed. The smartphone diagnostic odds ratio for moderate-to-severe OSAS (apnea/hypopnea index > 15) was 57.3873 (95% confidence interval [CI]: [34.7462; 94.7815]). The area under the summary receiver operating characteristic curve was 0.917. The sensitivity, specificity, negative predictive value, and positive predictive value were 0.9064 [0.8789; 0.9282], 0.8801 [0.8227; 0.9207], 0.9049 [0.8556; 0.9386], and 0.8844 [0.8234; 0.9263], respectively. We performed subgroup analysis based on the various OSAS detection methods (motion, sound, oximetry, and combinations thereof). Although the diagnostic odds ratios, specificities, and negative predictive values varied significantly (all p < 0.05), all methods afforded good sensitivity (> 80%). The sensitivities and positive predictive values were similar for the various methods (both p > 0.05). The mean difference with standard deviation in the AHI between smartphone and polysomnography was -0.6845 ± 1.611 events/h [-3.8426; 2.4735]. Conclusions Smartphone could be used to screen the moderate-to-severe OSAS. The mean difference between smartphones and polysomnography AHI measurements was small, though limits of agreement was wide. Therefore, clinicians should be cautious when making clinical decisions based on these devices.
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Affiliation(s)
- Do Hyun Kim
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sung Won Kim
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Se Hwan Hwang
- Department of Otolaryngology-Head and Neck Surgery, Bucheon St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- * E-mail:
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Dietz-Terjung S, Martin AR, Finnsson E, Ágústsson JS, Helgason S, Helgadóttir H, Welsner M, Taube C, Weinreich G, Schöbel C. Proof of principle study: diagnostic accuracy of a novel algorithm for the estimation of sleep stages and disease severity in patients with sleep-disordered breathing based on actigraphy and respiratory inductance plethysmography. Sleep Breath 2021; 25:1945-1952. [PMID: 33594617 PMCID: PMC8590674 DOI: 10.1007/s11325-021-02316-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 01/27/2021] [Accepted: 02/03/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE In this proof of principle study, we evaluated the diagnostic accuracy of the novel Nox BodySleepTM 1.0 algorithm (Nox Medical, Iceland) for the estimation of disease severity and sleep stages based on features extracted from actigraphy and respiratory inductance plethysmography (RIP) belts. Validation was performed against in-lab polysomnography (PSG) in patients with sleep-disordered breathing (SDB). METHODS Patients received PSG according to AASM. Sleep stages were manually scored using the AASM criteria and the recording was evaluated by the novel algorithm. The results were analyzed by descriptive statistics methods (IBM SPSS Statistics 25.0). RESULTS We found a strong Pearson correlation (r=0.91) with a bias of 0.2/h for AHI estimation as well as a good correlation (r=0.81) and an overestimation of 14 min for total sleep time (TST). Sleep efficiency (SE) was also valued with a good Pearson correlation (r=0.73) and an overestimation of 2.1%. Wake epochs were estimated with a sensitivity of 0.65 and a specificity of 0.59 while REM and non-REM (NREM) phases were evaluated a sensitivity of 0.72 and 0.74, respectively. Specificity was 0.74 for NREM and 0.68 for REM. Additionally, a Cohen's kappa of 0.62 was found for this 3-class classification problem. CONCLUSION The algorithm shows a moderate diagnostic accuracy for the estimation of sleep. In addition, the algorithm determines the AHI with good agreement with the manual scoring and it shows good diagnostic accuracy in estimating wake-sleep transition. The presented algorithm seems to be an appropriate tool to increase the diagnostic accuracy of portable monitoring. The validated diagnostic algorithm promises a more appropriate and cost-effective method if integrated in out-of-center (OOC) testing of patients with suspicion for SDB.
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Affiliation(s)
- Sarah Dietz-Terjung
- Faculty of Sleep Medicine and Telemedicine, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany.
- Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany.
| | - Amelie Ricarda Martin
- Faculty of Sleep Medicine and Telemedicine, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany
| | | | | | | | | | - Matthias Welsner
- Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany
| | - Christian Taube
- Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany
| | - Gerhard Weinreich
- Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany
| | - Christoph Schöbel
- Faculty of Sleep Medicine and Telemedicine, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany
- Department of Pulmonology, University Medicine Essen - Ruhrlandklinik, West German Lung Center, University Duisburg-Essen, Duisburg, Germany
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Tiron R, Lyon G, Kilroy H, Osman A, Kelly N, O'Mahony N, Lopes C, Coffey S, McMahon S, Wren M, Conway K, Fox N, Costello J, Shouldice R, Lederer K, Fietze I, Penzel T. Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology. J Thorac Dis 2020; 12:4476-4495. [PMID: 32944361 PMCID: PMC7475565 DOI: 10.21037/jtd-20-804] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background Obstructive sleep apnea (OSA) has a high prevalence, with an estimated 425 million adults with apnea hypopnea index (AHI) of ≥15 events/hour, and is significantly underdiagnosed. This presents a significant pain point for both the sufferers, and for healthcare systems, particularly in a post COVID-19 pandemic world. As such, it presents an opportunity for new technologies that can enable screening in both developing and developed countries. In this work, the performance of a non-contact OSA screener App that can run on both Apple and Android smartphones is presented. Methods The subtle breathing patterns of a person in bed can be measured via a smartphone using the "Firefly" app technology platform [and underpinning software development kit (SDK)], which utilizes advanced digital signal processing (DSP) technology and artificial intelligence (AI) algorithms to identify detailed sleep stages, respiration rate, snoring, and OSA patterns. The smartphone is simply placed adjacent to the subject, such as on a bedside table, night stand or shelf, during the sleep session. The system was trained on a set of 128 overnights recorded at a sleep laboratory, where volunteers underwent simultaneous full polysomnography (PSG), and "Firefly" smartphone app analysis. A separate independent test set of 120 recordings was collected across a range of Apple iOS and Android smartphones, and withheld for performance evaluation by a different team. An operating point tuned for mid-sensitivity (i.e., balancing sensitivity and specificity) was chosen for the screener. Results The performance on the test set is comparable to ambulatory OSA screeners, and other smartphone screening apps, with a sensitivity of 88.3% and specificity of 80.0% [with receiver operating characteristic (ROC) area under the curve (AUC) of 0.92], for a clinical threshold for the AHI of ≥15 events/hour of detected sleep time. Conclusions The "Firefly" app based sensing technology offers the potential to significantly lower the barrier of entry to OSA screening, as no hardware (other than the user's personal smartphone) is required. Additionally, multi-night analysis is possible in the home environment, without requiring the wearing of a portable PSG or other home sleep test (HST).
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Affiliation(s)
- Roxana Tiron
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | - Graeme Lyon
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | - Hannah Kilroy
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | - Ahmed Osman
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | - Nicola Kelly
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | | | - Cesar Lopes
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | - Sam Coffey
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | | | - Michael Wren
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | - Kieran Conway
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | - Niall Fox
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
| | - John Costello
- ResMed Sensor Technologies, NexusUCD, Dublin, Ireland
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Zaffaroni A, Coffey S, Dodd S, Kilroy H, Lyon G, O'Rourke D, Lederer K, Fietze I, Penzel T. Sleep Staging Monitoring Based on Sonar Smartphone Technology. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:2230-2233. [PMID: 31946344 DOI: 10.1109/embc.2019.8857033] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper presents the validation results of a new non-contact ultrasonic technology, which employs inaudible Sonar to monitor the movements and respiration of a subject in bed. Sleep monitoring can be achieved by placing a smartphone onto the bedside table and starting a custom app. The app employs sophisticated and novel proprietary algorithms to identify sleep stages: Wake (W), Light Sleep (N1, N2 sleep), Deep Sleep (N3 sleep), Rapid Eye Movement (REM) Sleep or Absence.The sleep staging performance of the app were assessed by testing it against expert manually scored polysomnography (PSG) of 38 subjects gathered in a sleep laboratory. As a secondary assessment, on the same dataset, the performance of the app is compared to that of a reference non-contact device, the S+ by ResMed.Performance across different sleep stage detections was balanced, exceeding the agreement typically reported for actigraphy based devices [1], [2] thanks to a significantly higher sensitivity for all sleep stages. Furthermore, the performance of the app was found to be comparable to the S+ by ResMed product [3], [4].The combination of unobtrusive non-contact sensing and accurate sleep quality assessment, coupled with removal of the requirement to purchase a custom device to enable monitoring of sleep, enables consumers to measure their sleep in the home environment in a zero-cost and accessible manner, while providing sleep staging information not otherwise available with actigraphy based devices.
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