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Aziz S, A M Ali A, Aslam H, A Abd-Alrazaq A, AlSaad R, Alajlani M, Ahmad R, Khalil L, Ahmed A, Sheikh J. Wearable Artificial Intelligence for Sleep Disorders: Scoping Review. J Med Internet Res 2025; 27:e65272. [PMID: 40327852 DOI: 10.2196/65272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2024] [Revised: 02/10/2025] [Accepted: 02/20/2025] [Indexed: 05/08/2025] Open
Abstract
BACKGROUND Worldwide, 30%-45% of adults have sleep disorders, which are linked to major health issues such as diabetes and cardiovascular disease. Long-term monitoring with traditional in-lab testing is impractical due to high costs. Wearable artificial intelligence (AI)-powered solutions offer accessible, scalable, and continuous monitoring, improving the identification and treatment of sleep problems. OBJECTIVE This scoping review aims to provide an overview of AI-powered wearable devices used for sleep disorders, focusing on study characteristics, wearable technology features, and AI methodologies for detection and analysis. METHODS Seven electronic databases (MEDLINE, PsycINFO, Embase, IEEE Xplore, ACM Digital Library, Google Scholar, and Scopus) were searched for peer-reviewed literature published before March 2024. Keywords were selected based on 3 domains: sleep disorders, AI, and wearable devices. The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. Study selection was conducted in 2 steps: first, by reviewing titles and abstracts, followed by full-text screening. Two reviewers independently conducted study selection and data extraction, resolving discrepancies by consensus. The extracted data were synthesized using a narrative approach. RESULTS The initial search yielded 615 articles, of which 46 met the eligibility criteria and were included in the final analysis. The majority of studies focused on sleep apnea. Wearable AI was widely deployed for diagnosing and screening disorders; however, none of the studies used it for treatment. Commercial devices were the most commonly used type of wearable technology, appearing in 30 out of 46 (65%) studies. Among these, various brands were utilized rather than a single large, well-known brand; 19 (41%) studies used wrist-worn devices. Respiratory data were used by 25 of 46 (54%) studies as the primary data for model development, followed by heart rate (22/46, 48%) and body movement (17/46, 37%). The most popular algorithm was the convolutional neural network, adopted by 17 of 46 (37%) studies, followed by random forest (14/46, 30%) and support vector machines (12/46, 26%). CONCLUSIONS Wearable AI technology offers promising solutions for sleep disorders. These devices can be used for screening and diagnosis; however, research on wearable technology for sleep disorders other than sleep apnea remains limited. To statistically synthesize performance and efficacy results, more reviews are needed. Technology companies should prioritize advancements such as deep learning algorithms and invest in wearable AI for treating sleep disorders, given its potential. Further research is necessary to validate machine learning techniques using clinical data from wearable devices and to develop useful analytics for data collection, monitoring, prediction, classification, and recommendation in the context of sleep disorders.
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Affiliation(s)
- Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Amal A M Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
- Social and Economic Survey Research Institute, Qatar University, Doha, Qatar
| | - Hania Aslam
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Alaa A Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Mohannad Alajlani
- Institute of Digital Healthcare, University of Warwick, Warwick, United Kingdom
| | - Reham Ahmad
- Institute of Digital Healthcare, University of Warwick, Warwick, United Kingdom
| | | | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
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Pinilla L, Chai‐Coetzer CL, Eckert DJ. Diagnostic Modalities in Sleep Disordered Breathing: Current and Emerging Technology and Its Potential to Transform Diagnostics. Respirology 2025; 30:286-302. [PMID: 40032579 PMCID: PMC11965016 DOI: 10.1111/resp.70012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2024] [Revised: 01/29/2025] [Accepted: 02/09/2025] [Indexed: 03/05/2025]
Abstract
Underpinned by rigorous clinical trial data, the use of existing home sleep apnoea testing is now commonly employed for sleep disordered breathing diagnostics in most clinical sleep centres globally. This has been a welcome addition for the field given the considerable burden of disease, cost, and access limitations with in-laboratory polysomnography testing. However, most existing home sleep apnoea testing approaches predominantly aim to replicate elements of conventional polysomnography in different forms with a focus on the estimation of the apnoea-hypopnoea index. New, simplified technology for sleep disordered breathing screening, detection/diagnosis, or monitoring has expanded exponentially in recent years. Emerging innovations in sleep monitoring technology now go beyond simple single-night replication of varying numbers of polysomnography signals in the home setting. These novel approaches have the potential to provide important new insights to overcome many of the existing limitations of sleep disordered breathing diagnostics and transform disease diagnosis and management to improve outcomes for patients. Accordingly, the current review summarises the existing evidence for sleep study testing in people with suspected sleep-related breathing disorders, discusses novel and emerging technologies and approaches according to three key categories: (1) wearables (e.g., body-worn sensors including wrist and finger sensors), (2) nearables (e.g., bed-embedded and bedside sensors), and (3) airables (e.g., audio and video recordings), and outlines their potential disruptive role to transform sleep disordered breathing diagnostics and care.
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Affiliation(s)
- Lucía Pinilla
- Adelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public HealthFlinders UniversityAdelaideAustralia
| | - Ching Li Chai‐Coetzer
- Adelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public HealthFlinders UniversityAdelaideAustralia
- Respiratory Sleep and Ventilation Services, Southern Adelaide Local Health NetworkFlinders Medical CentreBedford ParkAustralia
| | - Danny J. Eckert
- Adelaide Institute for Sleep Health and FHMRI Sleep Health, College of Medicine and Public HealthFlinders UniversityAdelaideAustralia
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Abd-Alrazaq A, Aslam H, AlSaad R, Alsahli M, Ahmed A, Damseh R, Aziz S, Sheikh J. Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis. J Med Internet Res 2024; 26:e58187. [PMID: 39255014 PMCID: PMC11422752 DOI: 10.2196/58187] [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: 03/08/2024] [Revised: 05/07/2024] [Accepted: 07/23/2024] [Indexed: 09/11/2024] Open
Abstract
BACKGROUND Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography. OBJECTIVE The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity. METHODS Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI's performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis. RESULTS Among 615 studies, 38 (6.2%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices. CONCLUSIONS Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses.
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Affiliation(s)
- Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Hania Aslam
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Rawan AlSaad
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Mohammed Alsahli
- Health Informatics Department, College of Health Science, Riyadh, Saudi Electronic university, Riyadh, Saudi Arabia
| | - Arfan Ahmed
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Rafat Damseh
- Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
| | - Sarah Aziz
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
| | - Javaid Sheikh
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Qatar Foundation, Doha, Qatar
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Olsen M, Zeitzer JM, Nakase-Richardson R, Musgrave VH, Sorensen HBD, Mignot E, Jennum PJ. A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies. IEEE Trans Biomed Eng 2024; 71:2506-2517. [PMID: 38498753 DOI: 10.1109/tbme.2024.3378480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.
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Chen T, Zhang J, Xu Z, Redmond SJ, Lovell NH, Liu G, Wang C. Energy-Efficient Sleep Apnea Detection Using a Hyperdimensional Computing Framework Based on Wearable Bracelet Photoplethysmography. IEEE Trans Biomed Eng 2024; 71:2483-2494. [PMID: 38483799 DOI: 10.1109/tbme.2024.3377270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2024]
Abstract
OBJECTIVE Sleep apnea syndrome (SAS) is a common sleep disorder, which has been shown to be an important contributor to major neurocognitive and cardiovascular sequelae. Considering current diagnostic strategies are limited with bulky medical devices and high examination expenses, a large number of cases go undiagnosed. To enable large-scale screening for SAS, wearable photoplethysmography (PPG) technologies have been used as an early detection tool. However, existing algorithms are energy-intensive and require large amounts of memory resources, which are believed to be the major drawbacks for further promotion of wearable devices for SAS detection. METHODS In this paper, an energy-efficient method of SAS detection based on hyperdimensional computing (HDC) is proposed. Inspired by the phenomenon of chunking in cognitive psychology as a memory mechanism for improving working memory efficiency, we proposed a one-dimensional block local binary pattern (1D-BlockLBP) encoding scheme combined with HDC to preserve dominant dynamical and temporal characteristics of pulse rate signals from wearable PPG devices. RESULTS Our method achieved 70.17 % accuracy in sleep apnea segment detection, which is comparable with traditional machine learning methods. Additionally, our method achieves up to 67× lower memory footprint, 68× latency reduction, and 93× energy saving on the ARM Cortex-M4 processor. CONCLUSION The simplicity of hypervector operations in HDC and the novel 1D-BlockLBP encoding effectively preserve pulse rate signal characteristics with high computational efficiency. SIGNIFICANCE This work provides a scalable solution for long-term home-based monitoring of sleep apnea, enhancing the feasibility of consistent patient care.
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Wang S, Xuan W, Chen D, Gu Y, Liu F, Chen J, Xia S, Dong S, Luo J. Machine Learning Assisted Wearable Wireless Device for Sleep Apnea Syndrome Diagnosis. BIOSENSORS 2023; 13:bios13040483. [PMID: 37185558 PMCID: PMC10136920 DOI: 10.3390/bios13040483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 05/17/2023]
Abstract
Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.
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Affiliation(s)
- Shaokui Wang
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Weipeng Xuan
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Ding Chen
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Yexin Gu
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Fuhai Liu
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Jinkai Chen
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
| | - Shudong Xia
- The Fourth Affiliated Hospital Zhejiang University School of Medicine, Yiwu 322000, China
| | - Shurong Dong
- Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China
| | - Jikui Luo
- Ministry of Education Key Laboratory of RF Circuits and Systems, College of Electronics & Information Hangzhou Dianzi University, Hangzhou 310018, China
- Key Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China
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Chen M, Wu S, Chen T, Wang C, Liu G. Information-Based Similarity of Ordinal Pattern Sequences as a Novel Descriptor in Obstructive Sleep Apnea Screening Based on Wearable Photoplethysmography Bracelets. BIOSENSORS 2022; 12:1089. [PMID: 36551056 PMCID: PMC9775447 DOI: 10.3390/bios12121089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Obstructive sleep apnea (OSA) is a common respiratory disorder associated with autonomic nervous system (ANS) dysfunction, resulting in abnormal heart rate variability (HRV). Capable of acquiring heart rate (HR) information with more convenience, wearable photoplethysmography (PPG) bracelets are proven to be a potential surrogate for electrocardiogram (ECG)-based devices. Meanwhile, bracelet-type PPG has been heavily marketed and widely accepted. This study aims to investigate the algorithm that can identify OSA with wearable devices. The information-based similarity of ordinal pattern sequences (OP_IBS), which is a modified version of the information-based similarity (IBS), has been proposed as a novel index to detect OSA based on wearable PPG signals. A total of 92 PPG recordings (29 normal subjects, 39 mild-moderate OSA subjects and 24 severe OSA subjects) were included in this study. OP_IBS along with classical indices were calculated. For severe OSA detection, the accuracy of OP_IBS was 85.9%, much higher than that of the low-frequency power to high-frequency power ratio (70.7%). The combination of OP_IBS, IBS, CV and LF/HF can achieve 91.3% accuracy, 91.0% sensitivity and 91.5% specificity. The performance of OP_IBS is significantly improved compared with our previous study based on the same database with the IBS method. In the Physionet database, OP_IBS also performed exceptionally well with an accuracy of 91.7%. This research shows that the OP_IBS method can access the HR dynamics of OSA subjects and help diagnose OSA in clinical environments.
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Affiliation(s)
- Mingjing Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-1112, USA
| | - Shan Wu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Tian Chen
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Changhong Wang
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
| | - Guanzheng Liu
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 518107, China
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Coluzzi D, Baselli G, Bianchi AM, Guerrero-Mora G, Kortelainen JM, Tenhunen ML, Mendez MO. Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device. SENSORS 2022; 22:s22145295. [PMID: 35890975 PMCID: PMC9323867 DOI: 10.3390/s22145295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 02/05/2023]
Abstract
Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects’ awareness.
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Affiliation(s)
- Davide Coluzzi
- Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, 20133 Milano, Italy; (A.M.B.); (M.O.M.)
- Correspondence: (D.C.); (G.B.)
| | - Giuseppe Baselli
- Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, 20133 Milano, Italy; (A.M.B.); (M.O.M.)
- Correspondence: (D.C.); (G.B.)
| | - Anna Maria Bianchi
- Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, 20133 Milano, Italy; (A.M.B.); (M.O.M.)
| | - Guillermina Guerrero-Mora
- Unidad Académica Multidisciplinaria Zona Media, Universidad Autónoma de San Luis Potosí, San Luis Potosí 79615, Mexico;
| | | | - Mirja L. Tenhunen
- Department of Clinical Neurophysiology, Medical Imaging Centre, Pirkanmaa Hospital District, Tampere, Finland;
- Department of Medical Physics, Tampere University Hospital, Medical Imaging Centre, Pirkanmaa Hospital District, Tampere, Finland
| | - Martin O. Mendez
- Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, 20133 Milano, Italy; (A.M.B.); (M.O.M.)
- Laboratorio Nacional—Centro de Investigación, Instrumentación e Imagenología Médica, Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí 78210, Mexico
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Benedetti D, Olcese U, Bruno S, Barsotti M, Maestri Tassoni M, Bonanni E, Siciliano G, Faraguna U. Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach. Nat Sci Sleep 2022; 14:941-956. [PMID: 35611177 PMCID: PMC9124490 DOI: 10.2147/nss.s352335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 02/27/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose A large portion of the adult population is thought to suffer from obstructive sleep apnoea syndrome (OSAS), a sleep-related breathing disorder associated with increased morbidity and mortality. International guidelines include the polysomnography and the cardiorespiratory monitoring (CRM) as diagnostic tools for OSAS, but they are unfit for a large-scale screening, given their invasiveness, high cost and lengthy process of scoring. Current screening methods are based on self-reported questionnaires that suffer from lack of objectivity. On the contrary, commercial smartbands are wearable devices capable of collecting accelerometric and photoplethysmographic data in a user-friendly and objective way. We questioned whether machine-learning (ML) classifiers trained on data collected through these wearable devices would help predict OSAS severity. Patients and Methods Each of the patients (n = 78, mean age ± SD: 57.2 ± 12.9 years; 30 females) underwent CRM and concurrently wore a commercial wrist smartband. CRM's traces were scored, and OSAS severity was reported as apnoea hypopnoea index (AHI). We trained three pairs of classifiers to make the following prediction: AHI <5 vs AHI ≥5, AHI <15 vs AHI ≥15, and AHI <30 vs AHI ≥30. Results According to the Matthews correlation coefficient (MCC), the proposed algorithms reached an overall good correlation with the ground truth (CRM) for AHI <5 vs AHI ≥5 (MCC: 0.4) and AHI <30 vs AHI ≥30 (MCC: 0.3) classifications. AHI <5 vs AHI ≥5 and AHI <30 vs AHI ≥30 classifiers' sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV) and diagnostic odds ratio (DOR) are comparable with the STOP-Bang questionnaire, an established OSAS screening tool. Conclusion Machine learning algorithms showed an overall good performance. Unlike questionnaires, these are based on objectively collected data. Furthermore, these commercial devices are widely distributed in the general population. The aforementioned advantages of machine-learning algorithms applied to smartbands' data over questionnaires lead to the conclusion that they could serve a population-scale screening for OSAS.
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Affiliation(s)
- Davide Benedetti
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Umberto Olcese
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | - Simone Bruno
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Marta Barsotti
- Neurological Clinics, University Hospital of Pisa, Pisa, Italy
| | - Michelangelo Maestri Tassoni
- Neurological Clinics, University Hospital of Pisa, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Enrica Bonanni
- Neurological Clinics, University Hospital of Pisa, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Gabriele Siciliano
- Neurological Clinics, University Hospital of Pisa, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Ugo Faraguna
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy
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Neumann D, Tiberius V, Biendarra F. Adopting wearables to customize health insurance contributions: a ranking-type Delphi. BMC Med Inform Decis Mak 2022; 22:112. [PMID: 35477495 PMCID: PMC9044726 DOI: 10.1186/s12911-022-01851-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/13/2022] [Indexed: 01/03/2023] Open
Abstract
Background Wearables, as small portable computer systems worn on the body, can track user fitness and health data, which can be used to customize health insurance contributions individually. In particular, insured individuals with a healthy lifestyle can receive a reduction of their contributions to be paid. However, this potential is hardly used in practice.
Objective This study aims to identify which barrier factors impede the usage of wearables for assessing individual risk scores for health insurances, despite its technological feasibility, and to rank these barriers according to their relevance. Methods To reach these goals, we conduct a ranking-type Delphi study with the following three stages. First, we collected possible barrier factors from a panel of 16 experts and consolidated them to a list of 11 barrier categories. Second, the panel was asked to rank them regarding their relevance. Third, to enhance the panel consensus, the ranking was revealed to the experts, who were then asked to re-rank the barriers. Results The results suggest that regulation is the most important barrier. Other relevant barriers are false or inaccurate measurements and application errors caused by the users. Additionally, insurers could lack the required technological competence to use the wearable data appropriately. Conclusion A wider use of wearables and health apps could be achieved through regulatory modifications, especially regarding privacy issues. Even after assuring stricter regulations, users’ privacy concerns could partly remain, if the data exchange between wearables manufacturers, health app providers, and health insurers does not become more transparent.
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