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Ontimare Manlises C, Chen JW, Huang CC. A gated recurrent unit model based on ultrasound images of dynamic tongue movement for determining the severity of obstructive sleep apnea. ULTRASONICS 2024; 141:107320. [PMID: 38678641 DOI: 10.1016/j.ultras.2024.107320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 04/14/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
Obstructive sleep apnea (OSA) presents as a respiratory disorder characterized by recurrent upper pharyngeal airway collapse during sleep. Dynamic tongue movement (DTM) analysis emerges as a promising avenue for elucidating the pathophysiological underpinnings of OSA, thereby facilitating its diagnosis. Recent endeavors have utilized artificial intelligence techniques to categorize OSA severity leveraging electrocardiography and blood oxygen saturation data. Nonetheless, the integration of ultrasound (US) imaging of the tongue remains largely untapped in the development of machine learning models aimed at determining the severity of OSA. This study endeavors to bridge this gap by capturing US images of DTM dynamics during wakefulness, encompassing transitions from normal breathing (NB) to the performance of the Müller maneuver (MM) in a cohort of 53 patients. Leveraging the modified optical flow method (MOFM), the trajectories of patients' DTM were tracked, facililtating the extraction of 27 parameters vital for model training. These parameters encompassed nine-point lateral movement, nine-point axial movement, and nine-point total displacement of the tongue, resulting in a dataset of 186,030 samples. The gated recurrent unit (GRU) method, renowned for its efficacy in motion tracking, was employed for model development in this study. Validation of the developed model was conducted via stratified k-fold cross-validation (SCV). The systems' overall performance in classifying OSA severity, as quantified by mean accuracy (MA), yielded a value of 43.49%. This pilot investigation marks an exploratory endeavor into the utilization of artificial intelligence for the classification of OSA severity based on US images and dynamic movement patterns. This novel model holds potential to assist clinicians in categorizing OSA severity and guiding the selection of pertinent treatment modalities tailored to the individual needs of patients afflicted with OSA.
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Affiliation(s)
- Cyrel Ontimare Manlises
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan; School of Electrical, Electronics, and Computer Engineering, Mapua University, Manila 1002 Philippines
| | - Jeng-Wen Chen
- Department of Otolaryngology-Head and Neck Surgery, Cardinal Tien Hospital and Schhool of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan; Department of Otolaryngology-Head and Neck Surgery, National Taiwan University Hospital, Taipei, Taiwan
| | - Chih-Chung Huang
- Department of Biomedical Engineering, National Cheng Kung University, Tainan, Taiwan.
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2
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Irish LA, Bottera AR, Manasse SM, Christensen Pacella KA, Schaefer LM. The Integration of Sleep Research Into Eating Disorders Research: Recommendations and Best Practices. Int J Eat Disord 2024. [PMID: 38937938 DOI: 10.1002/eat.24241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/01/2024] [Accepted: 05/23/2024] [Indexed: 06/29/2024]
Abstract
OBJECTIVE Sleep disturbance is common among individuals with eating disorders (EDs), with approximately 50% of patients with EDs reporting sleep disturbance. Sleep problems may promote, exacerbate, or maintain ED symptoms through a variety of hypothesized mechanisms, such as impaired executive function, increased negative affect, and disruptions to appetitive rhythms. Although research investigating the role of sleep in EDs is growing, the current literature suffers from methodological limitations and inconsistencies, which reduce our ability to translate findings to improve clinical practice. The purpose of this forum is to propose a coordinated approach to more seamlessly integrate sleep research into ED research with particular emphasis on best practices in the definition and assessment of sleep characteristics. METHODS In this article, we will describe the current status of sleep-related research and relevant gaps within ED research practices, define key sleep characteristics, and review common assessment strategies for these sleep characteristics. Throughout the forum, we also discuss study design considerations and recommendations for future research aiming to integrate sleep research into ED research. RESULTS/DISCUSSION Given the potential role of sleep in ED maintenance and treatment, it is important to build upon preliminary findings using a rigorous and systematic approach. Moving forward as a field necessitates a common lens through which future research on sleep and EDs may be conducted, communicated, and evaluated.
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Affiliation(s)
- Leah A Irish
- Department of Psychology, North Dakota State University, Fargo, North Dakota, USA
- Sanford Research, Center for Biobehavioral Research, Fargo, North Dakota, USA
| | | | - Stephanie M Manasse
- Center for Weight, Eating, and Lifestyle Sciences, Drexel University, Philadelphia, Pennsylvania, USA
- Department of Psychological Brain Sciences, Drexel University, Philadelphia, Pennsylvania, USA
| | | | - Lauren M Schaefer
- Sanford Research, Center for Biobehavioral Research, Fargo, North Dakota, USA
- Department of Psychiatry, University of North Dakota School of Medicine and Health Sciences, Fargo, North Dakota, USA
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3
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Zhang B, Zhao M, Zhang X, Zhang X, Liu X, Huang W, Lu S, Xu J, Liu Y, Xu W, Li X, Tang J. The value of circadian heart rate variability for the estimation of obstructive sleep apnea severity in adult males. Sleep Breath 2024; 28:1105-1118. [PMID: 38170376 DOI: 10.1007/s11325-023-02983-1] [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: 09/14/2023] [Revised: 12/05/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVES Heart rate variability (HRV) is becoming more prevalent as a measurable parameter in wearable sleep-monitoring devices, which are simple and effective instruments for illness evaluation. Currently, most studies on investigating OSA severity and HRV have measured heart rates during wakefulness or sleep. Therefore, the objective of this study was to investigate the circadian rhythm of HRV in male patients with OSA and its value for the estimation of OSA severity using group-based trajectory modeling. METHODS Patients with complaints of snoring were enrolled from the Sleep Center of Shandong Qianfoshan Hospital. Patients were divided into 3 groups according to apnea hypopnea index (AHI in events/h), as follows: (<15, 15≤AHI<30, and ≥30). HRV parameters were calculated using 24 h Holter monitoring, which included time-domain and frequency-domain indices. Circadian differences in the standard deviation of normal to normal (SDNN) were evaluated for OSA severity using analysis of variance, trajectory analysis, and multinomial logistic regression. RESULTS A total of 228 patients were enrolled, 47 with mild OSA, 48 moderate, and 133 severe. Patients with severe OSA exhibited reduced triangular index and higher very low frequency than those in the other groups. Circadian HRV showed that nocturnal SDNN was considerably higher than daytime SDNN in patients with severe OSA. The difference among the OSA groups was significant at 23, 24, 2, and 3 o'clock sharp between the severe and moderate OSA groups (all P<0.05). The heterogeneity of circadian HRV trajectories in OSA was strongly associated with OSA severity, including sleep structure and hypoxia-related parameters. Among the low-to-low, low-to-high, high-to-low, and high-to-high groups, OSA severity in the low-to-high group was the most severe, especially compared with the low-to-low and high-to-low SDNN groups, respectively. CONCLUSIONS Circadian HRV in patients with OSA emerged as low daytime and high nocturnal in SDNN, particularly in men with severe OSA. The heterogeneity of circadian HRV revealed that trajectories with low daytime and significantly high nighttime were more strongly associated with severe OSA. Thus, circadian HRV trajectories may be useful to identify the severity of OSA.
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Affiliation(s)
- Baokun Zhang
- Department of Neurology, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, NO. 16766, Jingshi Road, Jinan, Shandong, 250014, People's Republic of China
| | - Mengke Zhao
- Stem Cell Clinical Research Center, National Joint Engineering Laboratory, Regenerative Medicine Center, The First Affiliated Hospital of Dalian Medical University, Dalian Innovation Institute of Stem Cell and Precision Medicine, Dalian, Liaoning Province, China
| | - Xiao Zhang
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China
| | - Xiaoyu Zhang
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China
| | - Xiaomin Liu
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China
| | - Weiwei Huang
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China
| | - Shanshan Lu
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China
| | - Juanjuan Xu
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China
| | - Ying Liu
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China
| | - Wei Xu
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China
| | - Xiuhua Li
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China.
| | - Jiyou Tang
- Department of Neurology, Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, NO. 16766, Jingshi Road, Jinan, Shandong, 250014, People's Republic of China.
- Department of Neurology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong institute of Neuroimmunology, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, Shandong, China.
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Armoundas AA, Ahmad FS, Bennett DA, Chung MK, Davis LL, Dunn J, Narayan SM, Slotwiner DJ, Wiley KK, Khera R. Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e000095. [PMID: 38779844 DOI: 10.1161/hcg.0000000000000095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.
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Balkan N, Çavuşoğlu M, Hornung R. Application of portable sleep monitoring devices in pregnancy: a comprehensive review. Physiol Meas 2024; 45:05TR01. [PMID: 38663417 DOI: 10.1088/1361-6579/ad43ad] [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: 09/25/2023] [Accepted: 04/25/2024] [Indexed: 05/31/2024]
Abstract
Objective.The physiological, hormonal and biomechanical changes during pregnancy may trigger sleep disordered breathing (SDB) in pregnant women. Pregnancy-related sleep disorders may associate with adverse fetal and maternal outcomes including gestational diabetes, preeclampsia, preterm birth and gestational hypertension. Most of the screening and diagnostic studies that explore SDB during pregnancy were based on questionnaires which are inherently limited in providing definitive conclusions. The current gold standard in diagnostics is overnight polysomnography (PSG) involving the comprehensive measurements of physiological changes during sleep. However, applying the overnight laboratory PSG on pregnant women is not practical due to a number of challenges such as patient inconvenience, unnatural sleep dynamics, and expenses due to highly trained personnel and technology. Parallel to the progress in wearable sensors and portable electronics, home sleep monitoring devices became indispensable tools to record the sleep signals of pregnant women at her own sleep environment. This article reviews the application of portable sleep monitoring devices in pregnancy with particular emphasis on estimating the perinatal outcomes.Approach.The advantages and disadvantages of home based sleep monitoring systems compared to subjective sleep questionnaires and overnight PSG for pregnant women were evaluated.Main Results.An overview on the efficiency of the application of home sleep monitoring in terms of accuracy and specificity were presented for particular fetal and maternal outcomes.Significance.Based on our review, more homogenous and comparable research is needed to produce conclusive results with home based sleep monitoring systems to study the epidemiology of SDB in pregnancy and its impact on maternal and neonatal health.
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Affiliation(s)
- Nürfet Balkan
- Department of Gynecology, University Hospital Zurich, Frauenklinikstrasse 10, 8006 Zurich, Switzerland
| | - Mustafa Çavuşoğlu
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, 8091 Zürich, Switzerland
| | - René Hornung
- Department of Gynecology, University Hospital Zurich, Frauenklinikstrasse 10, 8006 Zurich, Switzerland
- Gynecology and Obstetrics Department, Kantonspital St Gallen, Rorschacherstrasse 95, 9007 St Gallen, Switzerland
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Park JH, Wang C, Shin H. FDA-cleared home sleep apnea testing devices. NPJ Digit Med 2024; 7:123. [PMID: 38740907 DOI: 10.1038/s41746-024-01112-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/12/2024] [Indexed: 05/16/2024] Open
Abstract
The demand for home sleep apnea testing (HSAT) devices is escalating, particularly in the context of the coronavirus 2019 (COVID-19) pandemic. The absence of standardized development and verification procedures poses a significant challenge. This study meticulously analyzed the approval process characteristics of HSAT devices by the U.S. Food and Drug Administration (FDA) from September 1, 2003, to September 1, 2023, with a primary focus on ensuring safety and clinical effectiveness. We examined 58 reports out of 1046 that underwent FDA clearance via the 510(k) and de novo pathways. A substantial surge in certifications after the 2022 pandemic was observed. Type-3 devices dominated, signifying a growing trend for both home and clinical use. Key measurement items included respiration and sleep analysis, with the apnea-hypopnea index (AHI) and sleep stage emerging as pivotal indicators. The majority of FDA-cleared HSAT devices adhered to electrical safety and biocompatibility standards. Critical considerations encompass performance and function testing, usability, and cybersecurity. This study emphasized the nearly indispensable role of clinical trials in ensuring the clinical effectiveness of HSAT devices. Future studies should propose guidances that specify stringent requirements, robust clinical trial designs, and comprehensive performance criteria to guarantee the minimum safety and clinical effectiveness of HSATs.
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Affiliation(s)
- Ji Hyeun Park
- Department of Convergence Medicine, Asan Medical Center, Brain Korea 21 Project, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Changwon Wang
- Biomedical Engineering Research Center, Asan Medical Center, Seoul, 05505, Republic of Korea
| | - Hangsik Shin
- Department of Convergence Medicine, Asan Medical Center, Brain Korea 21 Project, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
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Hu H. A corn leaf based-strain sensor and triboelectric nanogenerator for running monitoring and energy harvesting. Heliyon 2024; 10:e29025. [PMID: 38601652 PMCID: PMC11004563 DOI: 10.1016/j.heliyon.2024.e29025] [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] [Received: 01/09/2024] [Revised: 03/27/2024] [Accepted: 03/28/2024] [Indexed: 04/12/2024] Open
Abstract
Recently, advanced wearable devices with posture sensing and energy harvesting have received widespread attention. Thus, we proposed a dual-function device (energy harvesting and running posture sensing), including carbon attached corn leaf strain sensor (CC-strain sensor) and a corn leaf-based triboelectric nanogenerator (C-TENG).According to the results, the relative resistance rate (ΔR/R0) exhibits linear characteristics in the three strain regions, and its linear coefficients are all above 0.96. Besides, at low strain rates from 0.01% to 0.1%, the CC-strain sensor can reach high sensitivity for monitoring weak signals, such as expressions in dance performances. The C-TENG device can achieve mechanical energy harvesting, providing a way to power low-power portable devices. From the results, the maximum power of C-TENG can arrive at 222 μW (resistance: 100 MΩ). This research can provide a new path to integrate strain sensors and TENG devices in running monitoring.
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Affiliation(s)
- Huifang Hu
- Chengdu Sport University, Chengdu, Sichuan Province, 610041, China
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de Zambotti M, Goldstein C, Cook J, Menghini L, Altini M, Cheng P, Robillard R. State of the science and recommendations for using wearable technology in sleep and circadian research. Sleep 2024; 47:zsad325. [PMID: 38149978 DOI: 10.1093/sleep/zsad325] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 12/21/2023] [Indexed: 12/28/2023] Open
Abstract
Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.
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Affiliation(s)
- Massimiliano de Zambotti
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
- Lisa Health Inc., Oakland, CA, USA
| | - Cathy Goldstein
- Sleep Disorders Center, Department of Neurology, University of Michigan-Ann Arbor, Ann Arbor, MI, USA
| | - Jesse Cook
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
| | - Marco Altini
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip Cheng
- Sleep Disorders and Research Center, Henry Ford Health, Detroit, MI, USA
| | - Rebecca Robillard
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
- Canadian Sleep Research Consortium, Canada
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Zhai B, Elder GJ, Godfrey A. Challenges and opportunities of deep learning for wearable-based objective sleep assessment. NPJ Digit Med 2024; 7:85. [PMID: 38575794 PMCID: PMC10995158 DOI: 10.1038/s41746-024-01086-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 03/22/2024] [Indexed: 04/06/2024] Open
Affiliation(s)
- Bing Zhai
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK
| | - Greg J Elder
- Northumbria Sleep Research, Department of Psychology, Northumbria University, Newcastle upon Tyne, UK
| | - Alan Godfrey
- Department of Computer and Information Sciences, Northumbria University, Newcastle, UK.
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10
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Birrer V, Elgendi M, Lambercy O, Menon C. Evaluating reliability in wearable devices for sleep staging. NPJ Digit Med 2024; 7:74. [PMID: 38499793 PMCID: PMC10948771 DOI: 10.1038/s41746-024-01016-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/18/2024] [Indexed: 03/20/2024] Open
Abstract
Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.
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Affiliation(s)
- Vera Birrer
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Carlo Menon
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
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11
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Maltezos A, Perrault AA, Walsh NA, Phillips EM, Gong K, Tarelli L, Smith D, Cross NE, Pomares FB, Gouin JP, Dang-Vu TT. Methodological approach to sleep state misperception in insomnia disorder: Comparison between multiple nights of actigraphy recordings and a single night of polysomnography recording. Sleep Med 2024; 115:21-29. [PMID: 38325157 DOI: 10.1016/j.sleep.2024.01.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/11/2023] [Accepted: 01/28/2024] [Indexed: 02/09/2024]
Abstract
STUDY OBJECTIVE To provide a comprehensive assessment of sleep state misperception in insomnia disorder (INS) and good sleepers (GS) by comparing recordings performed for one night in-lab (PSG and night review) and during several nights at-home (actigraphy and sleep diaries). METHODS Fifty-seven INS and 29 GS wore an actigraphy device and filled a sleep diary for two weeks at-home. They subsequently completed a PSG recording and filled a night review in-lab. Sleep perception index (subjective/objective × 100) of sleep onset latency (SOL), sleep duration (TST) and wake duration (TST) were computed and compared between methods and groups. RESULTS GS displayed a tendency to overestimate TST and WASO but correctly perceived SOL. The degree of misperception was similar across methods within the GS group. In contrast, INS underestimated their TST and overestimated their SOL both in-lab and at-home, yet the severity of misperception of SOL was larger at-home than in-lab. Finally, INS overestimated WASO only in-lab while correctly perceiving it at-home. While only the degree of TST misperception was stable across methods in INS, misperception of SOL and WASO were dependent on the method used. CONCLUSIONS We found that GS and INS exhibit opposite patterns and severity of sleep misperception. While the degree of misperception in GS was similar across methods, only sleep duration misperception was reliably detected by both in-lab and at-home methods in INS. Our results highlight that, when assessing sleep misperception in insomnia disorder, the environment and method of data collection should be carefully considered.
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Affiliation(s)
- Antonia Maltezos
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada; Department of Neuroscience, Université de Montreal, Montreal, QC, Canada
| | - Aurore A Perrault
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada.
| | - Nyissa A Walsh
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada; Department of Psychology & Centre for Clinical Research in Health, Concordia University, Montreal, QC, Canada
| | - Emma-Maria Phillips
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada; Department of Neuroscience, Université de Montreal, Montreal, QC, Canada
| | - Kirsten Gong
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada; Department of Psychology & Centre for Clinical Research in Health, Concordia University, Montreal, QC, Canada
| | - Lukia Tarelli
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada; Department of Psychology & Centre for Clinical Research in Health, Concordia University, Montreal, QC, Canada
| | - Dylan Smith
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
| | - Nathan E Cross
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada
| | - Florence B Pomares
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada
| | - Jean-Philippe Gouin
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada; Department of Psychology & Centre for Clinical Research in Health, Concordia University, Montreal, QC, Canada
| | - Thien Thanh Dang-Vu
- Sleep, Cognition and Neuroimaging Lab, Department of Health, Kinesiology and Applied Physiology & Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, CIUSSS Centre-Sud-de-l'Ile-de-Montréal, QC, Canada; Department of Neuroscience, Université de Montreal, Montreal, QC, Canada.
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12
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Mayeli A, Wilson JD, Donati FL, Ferrarelli F. Reduced slow wave density is associated with worse positive symptoms in clinical high risk: An objective readout of symptom severity for early treatment interventions? Psychiatry Res 2024; 333:115756. [PMID: 38281453 PMCID: PMC10923118 DOI: 10.1016/j.psychres.2024.115756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/13/2023] [Accepted: 01/24/2024] [Indexed: 01/30/2024]
Abstract
Individuals at clinical high risk for psychosis (CHR) present subsyndromal psychotic symptoms that can escalate and lead to the transition to a diagnosable psychotic disorder. Identifying biological parameters that are sensitive to these symptoms can therefore help objectively assess their severity and guide early interventions in CHR. Reduced slow wave oscillations (∼1 Hz) during non-rapid eye movement sleep were recently observed in first-episode psychosis patients and were linked to the intensity of their positive symptoms. Here, we collected overnight high-density EEG recordings from 37 CHR and 32 healthy control (HC) subjects and compared slow wave (SW) activity and other SW parameters (i.e., density and negative peak amplitude) between groups. We also assessed the relationships between clinical symptoms and SW parameters in CHR. While comparisons between HC and the entire CHR group showed no SW differences, CHR individuals with higher positive symptom severity (N = 18) demonstrated a reduction in SW density in an EEG cluster involving bilateral prefrontal, parietal, and right occipital regions compared to matched HC individuals. Furthermore, we observed a negative correlation between SW density and positive symptoms across CHR individuals, suggesting a potential target for early treatment interventions.
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Affiliation(s)
- Ahmad Mayeli
- Department of Psychiatry, University of Pittsburgh, USA
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13
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Ding Y, Jiang J, Wu Y, Zhang Y, Zhou J, Zhang Y, Huang Q, Zheng Z. Porous Conductive Textiles for Wearable Electronics. Chem Rev 2024; 124:1535-1648. [PMID: 38373392 DOI: 10.1021/acs.chemrev.3c00507] [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: 02/21/2024]
Abstract
Over the years, researchers have made significant strides in the development of novel flexible/stretchable and conductive materials, enabling the creation of cutting-edge electronic devices for wearable applications. Among these, porous conductive textiles (PCTs) have emerged as an ideal material platform for wearable electronics, owing to their light weight, flexibility, permeability, and wearing comfort. This Review aims to present a comprehensive overview of the progress and state of the art of utilizing PCTs for the design and fabrication of a wide variety of wearable electronic devices and their integrated wearable systems. To begin with, we elucidate how PCTs revolutionize the form factors of wearable electronics. We then discuss the preparation strategies of PCTs, in terms of the raw materials, fabrication processes, and key properties. Afterward, we provide detailed illustrations of how PCTs are used as basic building blocks to design and fabricate a wide variety of intrinsically flexible or stretchable devices, including sensors, actuators, therapeutic devices, energy-harvesting and storage devices, and displays. We further describe the techniques and strategies for wearable electronic systems either by hybridizing conventional off-the-shelf rigid electronic components with PCTs or by integrating multiple fibrous devices made of PCTs. Subsequently, we highlight some important wearable application scenarios in healthcare, sports and training, converging technologies, and professional specialists. At the end of the Review, we discuss the challenges and perspectives on future research directions and give overall conclusions. As the demand for more personalized and interconnected devices continues to grow, PCT-based wearables hold immense potential to redefine the landscape of wearable technology and reshape the way we live, work, and play.
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Affiliation(s)
- Yichun Ding
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, P. R. China
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350108, P. R. China
- Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, Fujian 350108, P. R. China
| | - Jinxing Jiang
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, P. R. China
| | - Yingsi Wu
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, P. R. China
| | - Yaokang Zhang
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, P. R. China
| | - Junhua Zhou
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, P. R. China
| | - Yufei Zhang
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, P. R. China
| | - Qiyao Huang
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, P. R. China
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong SAR 999077, P. R. China
| | - Zijian Zheng
- School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, P. R. China
- Department of Applied Biology and Chemical Technology, Faculty of Science, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR 999077, P. R. China
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong SAR 999077, P. R. China
- Research Institute for Smart Energy, The Hong Kong Polytechnic University, Hong Kong SAR 999077, P. R. China
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14
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Seol J, Chiba S, Kawana F, Tsumoto S, Masaki M, Tominaga M, Amemiya T, Tani A, Hiei T, Yoshimine H, Kondo H, Yanagisawa M. Validation of sleep-staging accuracy for an in-home sleep electroencephalography device compared with simultaneous polysomnography in patients with obstructive sleep apnea. Sci Rep 2024; 14:3533. [PMID: 38347028 PMCID: PMC10861536 DOI: 10.1038/s41598-024-53827-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/05/2024] [Indexed: 02/15/2024] Open
Abstract
Efforts to simplify standard polysomnography (PSG) in laboratories, especially for obstructive sleep apnea (OSA), and assess its agreement with portable electroencephalogram (EEG) devices are limited. We aimed to evaluate the agreement between a portable EEG device and type I PSG in patients with OSA and examine the EEG-based arousal index's ability to estimate apnea severity. We enrolled 77 Japanese patients with OSA who underwent simultaneous type I PSG and portable EEG monitoring. Combining pulse rate, oxygen saturation (SpO2), and EEG improved sleep staging accuracy. Bland-Altman plots, paired t-tests, and receiver operating characteristics curves were used to assess agreement and screening accuracy. Significant small biases were observed for total sleep time, sleep latency, awakening after falling asleep, sleep efficiency, N1, N2, and N3 rates, arousal index, and apnea indexes. All variables showed > 95% agreement in the Bland-Altman analysis, with interclass correlation coefficients of 0.761-0.982, indicating high inter-instrument validity. The EEG-based arousal index demonstrated sufficient power for screening AHI ≥ 15 and ≥ 30 and yielded promising results in predicting apnea severity. Portable EEG device showed strong agreement with type I PSG in patients with OSA. These suggest that patients with OSA may assess their condition at home.
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Affiliation(s)
- Jaehoon Seol
- Faculty of Health and Sports Sciences, University of Tsukuba, Tsukuba, Ibaraki, 305-8574, Japan.
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, 1-2 Kasuga, Tsukuba, Ibaraki, 305-8550, Japan.
- Department of Frailty Research, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan.
| | - Shigeru Chiba
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, 1-2 Kasuga, Tsukuba, Ibaraki, 305-8550, Japan
| | - Fusae Kawana
- Cardiovascular Respiratory Sleep Medicine, Juntendo University Graduate School of Medicine, Tokyo, 113-8421, Japan
| | - Saki Tsumoto
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, 1-2 Kasuga, Tsukuba, Ibaraki, 305-8550, Japan
- Ph.D. Program in Humanics, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan
| | - Minori Masaki
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, 1-2 Kasuga, Tsukuba, Ibaraki, 305-8550, Japan
- Ph.D. Program in Humanics, University of Tsukuba, Tsukuba, Ibaraki, 305-8575, Japan
| | | | | | | | | | - Hiroyuki Yoshimine
- Department of Respiratory Medicine, Inoue Hospital, Nagasaki, Nagasaki, 850-0045, Japan
| | - Hideaki Kondo
- Department of General Medicine, Institute of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki, 852-8102, Japan
| | - Masashi Yanagisawa
- International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, 1-2 Kasuga, Tsukuba, Ibaraki, 305-8550, Japan.
- S'UIMIN, Inc., Tokyo, 151-0061, Japan.
- Life Science Center for Survival Dynamics (TARA), University of Tsukuba, Ibaraki, 305-8577, Japan.
- R&D Center for Frontiers of Mirai in Policy and Technology (F-MIRAI), University of Tsukuba, Ibaraki, 305-8575, Japan.
- Department of Molecular Genetics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
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15
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Yook S, Kim D, Gupte C, Joo EY, Kim H. Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity. Sleep Med 2024; 114:211-219. [PMID: 38232604 PMCID: PMC10872216 DOI: 10.1016/j.sleep.2024.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 12/28/2023] [Accepted: 01/10/2024] [Indexed: 01/19/2024]
Abstract
BACKGROUND /Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening. METHODS An Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data. RESULTS Using RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants. CONCLUSIONS The proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.
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Affiliation(s)
- Soonhyun Yook
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA
| | - Dongyeop Kim
- Department of Neurology, Seoul Hospital, College of Medicine, Ewha Womans University, Seoul, 07804, South Korea
| | - Chaitanya Gupte
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Medical Center, Samsung Biomedical Research Institute, School of Medicine, Sungkyunkwan University, Seoul, 06351, South Korea.
| | - Hosung Kim
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, 90033, USA.
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Chato L, Regentova E. Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data. J Pers Med 2023; 13:1703. [PMID: 38138930 PMCID: PMC10744730 DOI: 10.3390/jpm13121703] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/01/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023] Open
Abstract
Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.
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Affiliation(s)
- Lina Chato
- Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA;
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17
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Mohamed M, Mohamed N, Kim JG. Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review. BIOSENSORS 2023; 13:1019. [PMID: 38131779 PMCID: PMC10741861 DOI: 10.3390/bios13121019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023]
Abstract
Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.
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Affiliation(s)
| | | | - Jae Gwan Kim
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea; (M.M.); (N.M.)
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Shajari S, Kuruvinashetti K, Komeili A, Sundararaj U. The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:9498. [PMID: 38067871 PMCID: PMC10708748 DOI: 10.3390/s23239498] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023]
Abstract
Disease diagnosis and monitoring using conventional healthcare services is typically expensive and has limited accuracy. Wearable health technology based on flexible electronics has gained tremendous attention in recent years for monitoring patient health owing to attractive features, such as lower medical costs, quick access to patient health data, ability to operate and transmit data in harsh environments, storage at room temperature, non-invasive implementation, mass scaling, etc. This technology provides an opportunity for disease pre-diagnosis and immediate therapy. Wearable sensors have opened a new area of personalized health monitoring by accurately measuring physical states and biochemical signals. Despite the progress to date in the development of wearable sensors, there are still several limitations in the accuracy of the data collected, precise disease diagnosis, and early treatment. This necessitates advances in applied materials and structures and using artificial intelligence (AI)-enabled wearable sensors to extract target signals for accurate clinical decision-making and efficient medical care. In this paper, we review two significant aspects of smart wearable sensors. First, we offer an overview of the most recent progress in improving wearable sensor performance for physical, chemical, and biosensors, focusing on materials, structural configurations, and transduction mechanisms. Next, we review the use of AI technology in combination with wearable technology for big data processing, self-learning, power-efficiency, real-time data acquisition and processing, and personalized health for an intelligent sensing platform. Finally, we present the challenges and future opportunities associated with smart wearable sensors.
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Affiliation(s)
- Shaghayegh Shajari
- Center for Applied Polymers and Nanotechnology (CAPNA), Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N1 N4, Canada;
- Center for Bio-Integrated Electronics (CBIE), Querrey Simpson Institute for Bioelectronics (QSIB), Northwestern University, Evanston, IL 60208, USA
| | - Kirankumar Kuruvinashetti
- Intelligent Human and Animal Assistive Devices, Department of Biomedical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.K.); (A.K.)
- Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Amin Komeili
- Intelligent Human and Animal Assistive Devices, Department of Biomedical Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada; (K.K.); (A.K.)
- Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Uttandaraman Sundararaj
- Center for Applied Polymers and Nanotechnology (CAPNA), Department of Chemical and Petroleum Engineering, University of Calgary, Calgary, AB T2N1 N4, Canada;
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Zhang D, Peng Z, Van Pul C, Overeem S, Chen W, Dudink J, Andriessen P, Aarts RM, Long X. Combining Cardiorespiratory Signals and Video-Based Actigraphy for Classifying Preterm Infant Sleep States. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1792. [PMID: 38002883 PMCID: PMC10670397 DOI: 10.3390/children10111792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/30/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023]
Abstract
The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants. The study enrolled eight preterm infants, and a total of 91 features were extracted from ECG, respiratory signals, and video-based actigraphy. By employing an extremely randomized trees (ET) algorithm and leave-one-subject-out cross-validation, a kappa score of 0.33 was achieved for the classification of AS, QS, and wake using cardiorespiratory features only. The kappa score significantly improved to 0.39 when incorporating eight video-based actigraphy features. Furthermore, the classification performance of AS and wake also improved, showing a kappa score increase of 0.21. These suggest that combining video-based actigraphy with cardiorespiratory signals can potentially enhance the performance of sleep-state classification in preterm infants. In addition, we highlighted the distinct strengths and limitations of video-based actigraphy and cardiorespiratory data in classifying specific sleep states.
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Affiliation(s)
- Dandan Zhang
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
- Department of Personal and Preventive Care, Philips Research, 5556 AE Eindhoven, The Netherlands
| | - Zheng Peng
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
- Department of Clinical Physics, Máxima Medical Center, 5504 DB Veldhoven, The Netherlands
| | - Carola Van Pul
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
- Department of Clinical Physics, Máxima Medical Center, 5504 DB Veldhoven, The Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
- Sleep Medicine Center, Kempenhaeghe, 5591 VE Heeze, The Netherlands
| | - Wei Chen
- The Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China;
| | - Jeroen Dudink
- Department of Neonatology, University Medical Center Utrecht, Wilhelmina Children’s Hospital, 3584 EA Utrecht, The Netherlands;
| | - Peter Andriessen
- Department of Neonatology, Máxima Medical Center, 5504 DB Veldhoven, The Netherlands;
| | - Ronald M. Aarts
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands; (D.Z.); (Z.P.); (C.V.P.); (S.O.); (R.M.A.)
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20
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Luo ZR, Bin-Yao, Huang ZY. Chronobiology discrepancies between patients with acute type a aortic dissection complicated with and without sleep apnea syndrome: a single-center seven-year retrospective study. BMC Cardiovasc Disord 2023; 23:508. [PMID: 37828436 PMCID: PMC10571263 DOI: 10.1186/s12872-023-03548-6] [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: 02/04/2023] [Accepted: 10/05/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND The present study aimed to investigate the differences in chronobiology and prevention between patients with acute type-A aortic dissection (ATAAD) complicated with sleep apnea syndrome (SAS) and without sleep apnea syndrome (non-SAS). METHODS We retrospectively analyzed the clinical information of ATAAD patients using hospital medical records and regional meteorological and chronological information between January 2013 and December 2019. RESULTS An early mortality rate of 16.9% (196 out of 1160 cases) was observed, comprising 95 cases of aortic rupture before surgery and 101 surgery-related deaths. Eighty-one of the 964 survivors were screened for SAS using complete morphological characteristics. Of these patients, 291 (33.0%) suffered from SAS, while 590 (67.0%) had no SAS. Based on a Circular Von Mises distribution analysis, the non-SAS patients experienced a significant morning peak in the occurrence of ATAAD at 10:04 (r1 = 0.148, p < 0.01). In contrast, the SAS patients experienced a significantly different (non-SAS vs. SAS, U2 = 0.947, p < 0.001) nighttime peak at 23:48 (r2 = 0.489, p < 0.01). Moreover, both non-SAS (Z = 39.770, P < 0.001) and SAS (Z = 55.663, P < 0.001) patients showed a comparable peak during January (non-SAS vs. SAS, U2 = 0.173, p > 0.05). Furthermore, SAS patients experienced a peak on Fridays (χ2 = 36.419, p < 0.001), whereas there was no significant difference in the weekly distribution in non-SAS patients (χ2 = 11.315, p = 0.079). CONCLUSIONS The analyses showed that both SAS and non-SAS patients showed distinct rhythmicity in ATAAD onset. These findings highlight the chronobiological triggers within different ATAAD subpopulations and may contribute to the prevention of this potentially fatal occurrence.
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Affiliation(s)
- Zeng-Rong Luo
- Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001 P. R. China
- Key Laboratory of Cardio-Thoracic Surgery, Fujian Medical University, Fujian Province University, Fuzhou, P. R. China
| | - Bin-Yao
- Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001 P. R. China
- Key Laboratory of Cardio-Thoracic Surgery, Fujian Medical University, Fujian Province University, Fuzhou, P. R. China
| | - Zhong-Yao Huang
- Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001 P. R. China
- Key Laboratory of Cardio-Thoracic Surgery, Fujian Medical University, Fujian Province University, Fuzhou, P. R. China
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21
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Tuohuti A, Lin Z, Cai J, Chen X. Can portable sleep monitors replace polysomnography for diagnosis of pediatric OSA: a systematic review and meta-analysis. Eur Arch Otorhinolaryngol 2023; 280:4351-4359. [PMID: 37405453 DOI: 10.1007/s00405-023-08095-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 06/26/2023] [Indexed: 07/06/2023]
Abstract
BACKGROUND Obstructive sleep apnoea (OSA) is an increasing health problem in children. The "gold standard" for OSA diagnosis at the moment is overnight polysomnography (PSG). Some researchers think portable monitors (PMs) are promising methods for diagnosing OSA, which make children more comfortable and lower costs. Compared with PSG, our comprehensively evaluated the diagnostic accuracy of PMs for diagnosing OSA in pediatrics. RESEARCH QUESTION This study aims to determine whether PMs can replace PSG in pediatric OSA diagnosis. STUDY DESIGN AND METHODS The PubMed, Embase, Medline databases Scopus, Web of Science, and Cochrane Library databases were searched systematically for studies published up to December 2022, evaluating the ability of PMs to diagnose OSA in children. For estimating the pooled sensitivity and specificity of the PMs in the included studies, we used a random-effects bivariate model. Studies included in this meta-analysis were evaluated systematically according to QUADAS-2 guidelines for assessing diagnostic accuracy studies. Two independent investigators conducted each stage of the review independently. RESULTS A total of 396 abstracts and 31 full-text articles were screened, and 41 full-text articles were chosen for final review. There were 707 pediatric patients enrolled in these twelve studies, and 9 PMs were evaluated. There was a wide range of diagnostic sensitivity and specificity among PM systems as compared to AHI measured by PSG. The pooled sensitivity and specificity in diagnosing pediatric OSA were, respectively, 0.91 [0.86, 0.94] and 0.76 [0.58, 0.88] for PMs. According to the summary receiver operating characteristic (SROC) curve, the AUC of PMs in diagnosing OSA in pediatric population was 0.93 [0.90, 0.95]. INTERPRETATION PMs were more sensitive but slightly less specific for pediatric OSA. The combination of PMs and questionnaires appeared to be a reliable tool for the diagnosis of pediatric OSA. This test may be used for screening subjects or populations at high risk of OSA when there is a high demand for PSG, but the quantity is limited. No clinical trial was involved in the current study.
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Affiliation(s)
- Aikebaier Tuohuti
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Zehua Lin
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Jie Cai
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China
| | - Xiong Chen
- Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, China.
- Sleep Medicine Centre, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
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22
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Chiang AA, Khosla S. Consumer Wearable Sleep Trackers: Are They Ready for Clinical Use? Sleep Med Clin 2023; 18:311-330. [PMID: 37532372 DOI: 10.1016/j.jsmc.2023.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2023]
Abstract
As the importance of good sleep continues to gain public recognition, the market for sleep-monitoring devices continues to grow. Modern technology has shifted from simple sleep tracking to a more granular sleep health assessment. We examine the available functionalities of consumer wearable sleep trackers (CWSTs) and how they perform in healthy individuals and disease states. Additionally, the continuum of sleep technology from consumer-grade to medical-grade is detailed. As this trend invariably grows, we urge professional societies to develop guidelines encompassing the practical clinical use of CWSTs and how best to incorporate them into patient care plans.
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Affiliation(s)
- Ambrose A Chiang
- Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Suite 2B-129, Cleveland, OH 44106, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Seema Khosla
- North Dakota Center for Sleep, 1531 32nd Avenue S Ste 103, Fargo, ND 58103, USA
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23
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Strumpf Z, Gu W, Tsai CW, Chen PL, Yeh E, Leung L, Cheung C, Wu IC, Strohl KP, Tsai T, Folz RJ, Chiang AA. Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea. Sleep Health 2023; 9:430-440. [PMID: 37380590 DOI: 10.1016/j.sleh.2023.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/25/2023] [Accepted: 05/03/2023] [Indexed: 06/30/2023]
Abstract
GOAL AND AIMS Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification. FOCUS TECHNOLOGY Belun Ring with second-generation deep learning algorithms REFERENCE TECHNOLOGY: In-lab polysomnography (PSG) SAMPLE: Eighty-four subjects (M: F = 1:1) referred for an overnight sleep study were eligible. Of these, 26% had PSG-AHI<5; 24% had PSG-AHI 5-15; 23% had PSG-AHI 15-30; 27% had PSG-AHI ≥ 30. DESIGN Rigorous performance evaluation by comparing Belun Ring to concurrent in-lab PSG using the 4% rule. CORE ANALYTICS Pearson's correlation coefficient, Student's paired t-test, diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, Cohen's kappa coefficient (kappa), Bland-Altman plots with bias and limits of agreement, receiver operating characteristics curves with area under the curve, and confusion matrix. CORE OUTCOMES The accuracy, sensitivity, specificity, and kappa in categorizing AHI ≥ 5 were 0.85, 0.92, 0.64, and 0.58, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 15 were 0.89, 0.91, 0.88, and 0.79, respectively. The accuracy, sensitivity, specificity, and Kappa in categorizing AHI ≥ 30 were 0.91, 0.83, 0.93, and 0.76, respectively. BSP2 also achieved an accuracy of 0.88 in detecting wake, 0.82 in detecting NREM, and 0.90 in detecting REM sleep. CORE CONCLUSION Belun Ring with second-generation algorithms detected OSA with good accuracy and demonstrated a moderate-to-substantial agreement in categorizing OSA severity and classifying sleep stages.
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Affiliation(s)
- Zachary Strumpf
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Wenbo Gu
- Belun Technology Company Limited, Hong Kong; Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | | | | | - Eric Yeh
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | | | | | - I-Chen Wu
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Kingman P Strohl
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA; Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Tiffany Tsai
- Case Western Reserve University, Cleveland, OH, USA
| | - Rodney J Folz
- Division of Pulmonary, Critical Care, and Sleep Medicine, Houston Methodist Hospital, Houston, TX, USA
| | - Ambrose A Chiang
- Division of Pulmonary, Critical Care, and Sleep Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Medicine, Case Western Reserve University, Cleveland, OH, USA; Division of Sleep Medicine, Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA.
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Liu Y, Li J, Xiao S, Liu Y, Bai M, Gong L, Zhao J, Chen D. Revolutionizing Precision Medicine: Exploring Wearable Sensors for Therapeutic Drug Monitoring and Personalized Therapy. BIOSENSORS 2023; 13:726. [PMID: 37504123 PMCID: PMC10377150 DOI: 10.3390/bios13070726] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/02/2023] [Accepted: 07/08/2023] [Indexed: 07/29/2023]
Abstract
Precision medicine, particularly therapeutic drug monitoring (TDM), is essential for optimizing drug dosage and minimizing toxicity. However, current TDM methods have limitations, including the need for skilled operators, patient discomfort, and the inability to monitor dynamic drug level changes. In recent years, wearable sensors have emerged as a promising solution for drug monitoring. These sensors offer real-time and continuous measurement of drug concentrations in biofluids, enabling personalized medicine and reducing the risk of toxicity. This review provides an overview of drugs detectable by wearable sensors and explores biosensing technologies that can enable drug monitoring in the future. It presents a comparative analysis of multiple biosensing technologies and evaluates their strengths and limitations for integration into wearable detection systems. The promising capabilities of wearable sensors for real-time and continuous drug monitoring offer revolutionary advancements in diagnostic tools, supporting personalized medicine and optimal therapeutic effects. Wearable sensors are poised to become essential components of healthcare systems, catering to the diverse needs of patients and reducing healthcare costs.
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Affiliation(s)
- Yuqiao Liu
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Junmin Li
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Shenghao Xiao
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yanhui Liu
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Mingxia Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Lixiu Gong
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jiaqian Zhao
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Dajing Chen
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310007, China
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25
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Tovar-Lopez FJ. Recent Progress in Micro- and Nanotechnology-Enabled Sensors for Biomedical and Environmental Challenges. SENSORS (BASEL, SWITZERLAND) 2023; 23:5406. [PMID: 37420577 DOI: 10.3390/s23125406] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023]
Abstract
Micro- and nanotechnology-enabled sensors have made remarkable advancements in the fields of biomedicine and the environment, enabling the sensitive and selective detection and quantification of diverse analytes. In biomedicine, these sensors have facilitated disease diagnosis, drug discovery, and point-of-care devices. In environmental monitoring, they have played a crucial role in assessing air, water, and soil quality, as well as ensured food safety. Despite notable progress, numerous challenges persist. This review article addresses recent developments in micro- and nanotechnology-enabled sensors for biomedical and environmental challenges, focusing on enhancing basic sensing techniques through micro/nanotechnology. Additionally, it explores the applications of these sensors in addressing current challenges in both biomedical and environmental domains. The article concludes by emphasizing the need for further research to expand the detection capabilities of sensors/devices, enhance sensitivity and selectivity, integrate wireless communication and energy-harvesting technologies, and optimize sample preparation, material selection, and automated components for sensor design, fabrication, and characterization.
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26
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Yue H, Li P, Li Y, Lin Y, Huang B, Sun L, Ma W, Fan X, Wen W, Lei W. Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis. J Clin Sleep Med 2023; 19:1017-1025. [PMID: 36734174 PMCID: PMC10235715 DOI: 10.5664/jcsm.10466] [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: 10/01/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 02/04/2023]
Abstract
STUDY OBJECTIVES We evaluated the validity of a squeeze-and-excitation and multiscaled fusion network (SE-MSCNN) using single-lead electrocardiogram (ECG) signals for obstructive sleep apnea detection and classification. METHODS Overnight polysomnographic data from 436 participants at the Sleep Center of the First Affiliated Hospital of Sun Yat-sen University were used to generate a new FAH-ECG dataset comprising 260, 88, and 88 single-lead ECG signal recordings for training, validation, and testing, respectively. The SE-MSCNN was employed for detection of apnea-hypopnea events from the acquired ECG segments. Sensitivity, specificity, accuracy, and F1 scores were assigned to assess algorithm performance. We also used the SE-MSCNN to estimate the apnea-hypopnea index, classify obstructive sleep apnea severity, and compare the agreement between 2 sleep technicians. RESULTS The SE-MSCNN's accuracy, sensitivity, specificity, and F1 score on the FAH-ECG dataset were 86.6%, 83.3%, 89.1%, and 0.843, respectively. Although slightly inferior to previously reported results using public datasets, it is superior to state-of-the-art open-source models. Furthermore, the SE-MSCNN had good agreement with manual scoring, such that the Spearman's correlations for the apnea-hypopnea index between the SE-MSCNN and 2 technicians were 0.93 and 0.94, respectively. Cohen's kappa scores in classifying the SE-MSCNN and the 2 sleep technicians were 0.72 and 0.78, respectively. CONCLUSIONS In this study, we validated the use of the SE-MSCNN in a clinical environment, and despite some limitations the network appeared to meet the performance standards for generalizability. Therefore, updating algorithms based on single-lead ECG signals can facilitate the development of novel wearable devices for efficient obstructive sleep apnea screening. CITATION Yue H, Li P, Li Y, et al. Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis. J Clin Sleep Med. 2023;19(6):1017-1025.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Pan Li
- School of Computer Science, South China Normal University, Guangzhou, People’s Republic of China
| | - Yun Li
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Yu Lin
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Bixue Huang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People’s Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People’s Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China
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Kwon S, Kim HS, Kwon K, Kim H, Kim YS, Lee SH, Kwon YT, Jeong JW, Trotti LM, Duarte A, Yeo WH. At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea. SCIENCE ADVANCES 2023; 9:eadg9671. [PMID: 37224243 DOI: 10.1126/sciadv.adg9671] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 04/17/2023] [Indexed: 05/26/2023]
Abstract
Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.
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Affiliation(s)
- Shinjae Kwon
- IEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Hyeon Seok Kim
- IEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Kangkyu Kwon
- IEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Hodam Kim
- IEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Yun Soung Kim
- Department of Radiology, Icahn School of Medicine at Mount Sinai, BioMedical Engineering and Imaging Institute, New York, NY 10029, USA
| | - Sung Hoon Lee
- IEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Young-Tae Kwon
- Metal Powder Department, Korea Institute of Materials Science, Changwon 51508, Republic of Korea
| | - Jae-Woong Jeong
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Lynn Marie Trotti
- Emory Sleep Center and Department of Neurology, Emory University School of Medicine, Atlanta, GA 30329, USA
| | - Audrey Duarte
- Department of Psychology, University of Texas at Austin, Austin, TX 78712, USA
| | - Woon-Hong Yeo
- IEN Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA 30332, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Institute for Materials, Neural Engineering Center, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Chikwetu L, Miao Y, Woldetensae MK, Bell D, Goldenholz DM, Dunn J. Does deidentification of data from wearable devices give us a false sense of security? A systematic review. Lancet Digit Health 2023; 5:e239-e247. [PMID: 36797124 PMCID: PMC10040444 DOI: 10.1016/s2589-7500(22)00234-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/30/2022] [Accepted: 12/01/2022] [Indexed: 02/16/2023]
Abstract
Wearable devices have made it easier to generate and share data collected on individuals. This systematic review seeks to investigate whether deidentifying data from wearable devices is sufficient to protect the privacy of individuals in datasets. We searched Web of Science, IEEE Xplore Digital Library, PubMed, Scopus, and the ACM Digital Library on Dec 6, 2021 (PROSPERO registration number CRD42022312922). We also performed manual searches in journals of interest until April 12, 2022. Although our search strategy had no language restrictions, all retrieved studies were in English. We included studies showing reidentification, identification, or authentication with data from wearable devices. Our search retrieved 17 625 studies, and 72 studies met our inclusion criteria. We designed a custom assessment tool for study quality and risk of bias assessments. 64 studies were classified as high quality and eight as moderate quality, and we did not detect any bias in any of the included studies. Correct identification rates were typically 86-100%, indicating a high risk of reidentification. Additionally, as little as 1-300 s of recording were required to enable reidentification from sensors that are generally not thought to generate identifiable information, such as electrocardiograms. These findings call for concerted efforts to rethink methods for data sharing to promote advances in research innovation while preventing the loss of individual privacy.
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Affiliation(s)
- Lucy Chikwetu
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Yu Miao
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Diarra Bell
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Daniel M Goldenholz
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
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Smith AA, Li R, Tse ZTH. Reshaping healthcare with wearable biosensors. Sci Rep 2023; 13:4998. [PMID: 36973262 PMCID: PMC10043012 DOI: 10.1038/s41598-022-26951-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/22/2022] [Indexed: 03/29/2023] Open
Abstract
Wearable health sensors could monitor the wearer's health and surrounding environment in real-time. With the development of sensor and operating system hardware technology, the functions of wearable devices have been gradually enriched with more diversified forms and more accurate physiological indicators. These sensors are moving towards high precision, continuity, and comfort, making great contributions to improving personalized health care. At the same time, in the context of the rapid development of the Internet of Things, the ubiquitous regulatory capabilities have been released. Some sensor chips are equipped with data readout and signal conditioning circuits, and a wireless communication module for transmitting data to computer equipment. At the same time, for data analysis of wearable health sensors, most companies use artificial neural networks (ANN). In addition, artificial neural networks could help users effectively get relevant health feedback. Through the physiological response of the human body, various sensors worn could effectively transmit data to the control unit, which analyzes the data and provides feedback of the health value to the user through the computer. This is the working principle of wearable sensors for health. This article focuses on wearable biosensors used for healthcare monitoring in different situations, as well as the development, technology, business, ethics, and future of wearable sensors for health monitoring.
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Affiliation(s)
- Aaron Asael Smith
- College of Engineering, University of Georgia, Athens, GA, 30602, USA
| | - Rui Li
- Tandon School of Engineering, New York University, New York, NY, 11201, USA
| | - Zion Tsz Ho Tse
- Department of Engineering and Material Science, Queen Mary University of London, London, E1 4NS, UK.
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Chen Y, Zhou E, Wang Y, Wu Y, Xu G, Chen L. The past, present, and future of sleep quality assessment and monitoring. Brain Res 2023; 1810:148333. [PMID: 36931581 DOI: 10.1016/j.brainres.2023.148333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/09/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023]
Abstract
Sleep quality is considered to be an individual's self-satisfaction with all aspects of the sleep experience. Good sleep not only improves a person's physical, mental and daily functional health, but also improves the quality-of-life level to some extent. In contrast, chronic sleep deprivation can increase the risk of diseases such as cardiovascular diseases, metabolic dysfunction and cognitive and emotional dysfunction, and can even lead to increased mortality. The scientific evaluation and monitoring of sleep quality is an important prerequisite for safeguarding and promoting the physiological health of the body. Therefore, we have compiled and reviewed the existing methods and emerging technologies commonly used for subjective and objective evaluation and monitoring of sleep quality, and found that subjective sleep evaluation is suitable for clinical screening and large-scale studies, while objective evaluation results are more intuitive and scientific, and in the comprehensive evaluation of sleep, if we want to get more scientific monitoring results, we should combine subjective and objective monitoring and dynamic monitoring.
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Affiliation(s)
- Yanyan Chen
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Enyuan Zhou
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Yu Wang
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Yuxiang Wu
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Guodong Xu
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Lin Chen
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China.
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Comparative study of a wearable intelligent sleep monitor and polysomnography monitor for the diagnosis of obstructive sleep apnea. Sleep Breath 2023; 27:205-212. [PMID: 35347656 PMCID: PMC9992231 DOI: 10.1007/s11325-022-02599-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/17/2022] [Accepted: 03/10/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Due to the lack of an objective population-based screening tool for obstructive sleep apnea (OSA), a large number of patients with potential OSA have not been identified in the general population. Our study compared an objective wearable sleep monitoring device with polysomnography (PSG) to provide a reference for OSA screening in a large population. METHODS Using a self-control method, patients admitted to our sleep center from July 2020 to March 2021 were selected for overnight PSG and wearable intelligent sleep monitor (WISM) at the same time. The sensitivity and specificity of the device for the diagnosis of OSA were evaluated. RESULTS A total of 196 participants (mean age: 45.1 ± 12.3 years [18-80 years]; 168 men [86%]) completed both PSG and WISM monitoring. Using an apnea-hypopnea index (AHI) ≥ 5 events/h as the diagnostic criterion, the sensitivity, specificity, kappa value, and area under the receiver operating characteristic curve of the WISM for OSA diagnosis were 93%, 77%, 0.6, and 0.95, respectively. Using an AHI ≥ 15 events/h as the diagnostic criterion for moderate-to-severe OSA, these values were 92%, 89%, 0.8, and 0.95, respectively. The mean difference in the AHI between PSG and the artificial intelligence oxygen desaturation index from the WISM was 6.8 events/h (95% confidence interval: - 13.1 to 26.7). CONCLUSION Compared with the PSG, WISM exhibits good sensitivity and specificity for the diagnosis of OSA. This small, simple, and easy-to-use device is more suitable for OSA screening in a large population because of its single-step application procedure.
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Radhakrishnan B. L., Ezra K, Jebadurai IJ. Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification. INTERNATIONAL JOURNAL OF E-COLLABORATION 2023. [DOI: 10.4018/ijec.316774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The smart world under Industry 4.0 is witnessing a notable spurt in sleep disorders and sleep-related issues in patients. Artificial intelligence and IoT are taking a giant leap in connecting sleep patients remotely with healthcare providers. The contemporary single-channel-based monitoring devices play a tremendous role in predicting sleep quality and related issues. Handcrafted feature extraction is a time-consuming job in machine learning-based automatic sleep classification. The proposed single-channel work uses Tsfresh to extract features from both the EEG channels (Pz-oz and Fpz-Cz) of the SEDFEx database individually to realise a single-channel EEG. The adopted mRMR feature selection approach selected 55 features from the extracted 787 features. A stacking ensemble classifier achieved 95%, 94%, 91%, and 88% accuracy using stratified 5-fold validation in 2, 3, 4, and 5 class classification employing healthy subjects data. The outcome of the experiments indicates that Tsfresh is an excellent tool to extract standard features from EEG signals.
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Affiliation(s)
- Radhakrishnan B. L.
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - Kirubakaran Ezra
- Department of Computer Science and Engineering, GRACE College of Engineering, Thoothukudi, India
| | - Immanuel Johnraja Jebadurai
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
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Kaur B, Kumar S, Kaushik BK. Novel Wearable Optical Sensors for Vital Health Monitoring Systems-A Review. BIOSENSORS 2023; 13:bios13020181. [PMID: 36831947 PMCID: PMC9954035 DOI: 10.3390/bios13020181] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 05/09/2023]
Abstract
Wearable sensors are pioneering devices to monitor health issues that allow the constant monitoring of physical and biological parameters. The immunity towards electromagnetic interference, miniaturization, detection of nano-volumes, integration with fiber, high sensitivity, low cost, usable in harsh environments and corrosion-resistant have made optical wearable sensor an emerging sensing technology in the recent year. This review presents the progress made in the development of novel wearable optical sensors for vital health monitoring systems. The details of different substrates, sensing platforms, and biofluids used for the detection of target molecules are discussed in detail. Wearable technologies could increase the quality of health monitoring systems at a nominal cost and enable continuous and early disease diagnosis. Various optical sensing principles, including surface-enhanced Raman scattering, colorimetric, fluorescence, plasmonic, photoplethysmography, and interferometric-based sensors, are discussed in detail for health monitoring applications. The performance of optical wearable sensors utilizing two-dimensional materials is also discussed. Future challenges associated with the development of optical wearable sensors for point-of-care applications and clinical diagnosis have been thoroughly discussed.
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Affiliation(s)
- Baljinder Kaur
- Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
| | - Santosh Kumar
- Shandong Key Laboratory of Optical Communication Science and Technology, School of Physics Science and Information Technology, Liaocheng University, Liaocheng 252059, China
- Correspondence: (S.K.); (B.K.K.)
| | - Brajesh Kumar Kaushik
- Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
- Correspondence: (S.K.); (B.K.K.)
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Ramasubramanian B, Reddy VS, Chellappan V, Ramakrishna S. Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases. BIOSENSORS 2022; 12:1176. [PMID: 36551143 PMCID: PMC9775999 DOI: 10.3390/bios12121176] [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: 11/09/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn't been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases.
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Affiliation(s)
- Brindha Ramasubramanian
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Vundrala Sumedha Reddy
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
| | - Vijila Chellappan
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), #08-03, 2 Fusionopolis Way, Innovis, Singapore 138634, Singapore
| | - Seeram Ramakrishna
- Department of Mechanical Engineering, Center for Nanofibers & Nanotechnology, National University of Singapore, Singapore 117574, Singapore
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35
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Hu Q, Li M, Li Y. Single-channel EEG signal extraction based on DWT, CEEMDAN, and ICA method. Front Hum Neurosci 2022; 16:1010760. [PMID: 36211125 PMCID: PMC9532603 DOI: 10.3389/fnhum.2022.1010760] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 08/29/2022] [Indexed: 12/04/2022] Open
Abstract
In special application scenarios, such as portable anesthesia depth monitoring, portable emotional state recognition and portable sleep monitoring, electroencephalogram (EEG) signal acquisition equipment is required to be convenient and easy to use. It is difficult to remove electrooculogram (EOG) artifacts when the number of EEG acquisition channels is small, especially when the number of observed signals is less than that of the source signals, and the overcomplete problem will arise. The independent component analysis (ICA) algorithm commonly used for artifact removal requires the number of basis vectors to be smaller than the dimension of the input data due to a set of standard orthonormal bases learned during the convergence process, so it cannot be used to solve the overcomplete problem. The empirical mode decomposition method decomposes the signal into several independent intrinsic mode functions so that the number of observed signals is more than that of the source signals, solving the overcomplete problem. However, when using this method to solve overcompleteness, the modal aliasing problem will arise, which is caused by abnormal events such as sharp signals, impulse interference, and noise. Aiming at the above problems, we propose a novel EEG artifact removal method based on discrete wavelet transform, complete empirical mode decomposition for adaptive noise (CEEMDAN) and ICA in this paper. First, the input signals are transformed by discrete wavelet (DWT), and then CEEMDAN is used to solve the overcomplete and mode aliasing problems, meeting the a priori conditions of the ICA algorithm. Finally, the components belonging to EOG artifacts are removed according to the sample entropy value of each independent component. Experiments show that this method can effectively remove EOG artifacts while solving the overcomplete and modal aliasing problems.
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Affiliation(s)
- Qinghui Hu
- School of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin, China
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Mingxin Li
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Yunde Li
- School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, China
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Vernon J, Canyelles-Pericas P, Torun H, Binns R, Ng WP, Wu Q, Fu YQ. Breath monitoring, sleep disorder detection, and tracking using thin-film acoustic waves and open-source electronics. NANOTECHNOLOGY AND PRECISION ENGINEERING 2022. [DOI: 10.1063/10.0013471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Apnoea, a major sleep disorder, affects many adults and causes several issues, such as fatigue, high blood pressure, liver conditions, increased risk of type II diabetes, and heart problems. Therefore, advanced monitoring and diagnosing tools of apnoea disorders are needed to facilitate better treatment, with advantages such as accuracy, comfort of use, cost effectiveness, and embedded computation capabilities to recognise, store, process, and transmit time series data. In this work we present an adaptation of our apnoea-Pi open-source surface acoustic wave (SAW) platform (Apnoea-Pi) to monitor and recognise apnoea in patients. The platform is based on a thin-film SAW device using bimorph ZnO and Al structures, including those fabricated as Al foils or plates, to achieve breath tracking based on humidity and temperature changes. We applied open-source electronics and provided embedded computing characteristics for signal processing, data recognition, storage, and transmission of breath signals. We show that the thin-film SAW device out-performed standard and off-the-shelf capacitive electronic sensors in terms of their response and accuracy for human breath-tracking purposes. This in combination with embedded electronics makes a suitable platform for human breath monitoring and sleep disorder recognition.
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Affiliation(s)
- Jethro Vernon
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Pep Canyelles-Pericas
- Department of Integrated Devices and Systems, MESA+ Institute for Nanotechnology, University of Twente, Enschede 7522 NB, The Netherlands
| | - Hamdi Torun
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Richard Binns
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Wai Pang Ng
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Qiang Wu
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
| | - Yong-Qing Fu
- Faculty of Engineering and Environment, University of Northumbria, Newcastle upon Tyne NE1 8ST, United Kingdom
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De Fazio R, Mattei V, Al-Naami B, De Vittorio M, Visconti P. Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview. MICROMACHINES 2022; 13:mi13081335. [PMID: 36014257 PMCID: PMC9412310 DOI: 10.3390/mi13081335] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 06/13/2023]
Abstract
Sleep is crucial for human health from metabolic, mental, emotional, and social points of view; obtaining good sleep in terms of quality and duration is fundamental for maintaining a good life quality. Over the years, several systems have been proposed in the scientific literature and on the market to derive metrics used to quantify sleep quality as well as detect sleep disturbances and disorders. In this field, wearable systems have an important role in the discreet, accurate, and long-term detection of biophysical markers useful to determine sleep quality. This paper presents the current state-of-the-art wearable systems and software tools for sleep staging and detecting sleep disorders and dysfunctions. At first, the paper discusses sleep's functions and the importance of monitoring sleep to detect eventual sleep disturbance and disorders. Afterward, an overview of prototype and commercial headband-like wearable devices to monitor sleep is presented, both reported in the scientific literature and on the market, allowing unobtrusive and accurate detection of sleep quality markers. Furthermore, a survey of scientific works related the effect of the COVID-19 pandemic on sleep functions, attributable to both infection and lifestyle changes. In addition, a survey of algorithms for sleep staging and detecting sleep disorders is introduced based on an analysis of single or multiple biosignals (EEG-electroencephalography, ECG-electrocardiography, EMG-electromyography, EOG-electrooculography, etc.). Lastly, comparative analyses and insights are provided to determine the future trends related to sleep monitoring systems.
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Affiliation(s)
- Roberto De Fazio
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
| | - Veronica Mattei
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
| | - Bassam Al-Naami
- Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, Zarqa 13133, Jordan
| | - Massimo De Vittorio
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
- Center for Biomolecular Nanotechnologies, Italian Technology Institute IIT, 73010 Arnesano, Italy
| | - Paolo Visconti
- Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
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Luo X, Wu Y, Niu L, Huang L. Bibliometric Analysis of Health Technology Research: 1990~2020. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9044. [PMID: 35897415 PMCID: PMC9330553 DOI: 10.3390/ijerph19159044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/19/2022] [Accepted: 07/19/2022] [Indexed: 12/10/2022]
Abstract
This paper aims to summarize the publishing trends, current status, research topics, and frontier evolution trends of health technology between 1990 and 2020 through various bibliometric analysis methods. In total, 6663 articles retrieved from the Web of Science core database were analyzed by Vosviewer and CiteSpace software. This paper found that: (1) The number of publications in the field of health technology increased exponentially; (2) there is no stable core group of authors in this research field, and the influence of the publishing institutions and journals in China is insufficient compared with those in Europe and the United States; (3) there are 21 core research topics in the field of health technology research, and these research topics can be divided into four classes: hot spots, potential hot spots, margin topics, and mature topics. C21 (COVID-19 prevention) and C10 (digital health technology) are currently two emerging research topics. (4) The number of research frontiers has increased in the past five years (2016-2020), and the research directions have become more diverse; rehabilitation, pregnancy, e-health, m-health, machine learning, and patient engagement are the six latest research frontiers.
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Affiliation(s)
| | | | | | - Lucheng Huang
- College of Economics and Management, Beijing University of Technology, Beijing 100124, China; (X.L.); (Y.W.); (L.N.)
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39
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Ates HC, Nguyen PQ, Gonzalez-Macia L, Morales-Narváez E, Güder F, Collins JJ, Dincer C. End-to-end design of wearable sensors. NATURE REVIEWS. MATERIALS 2022; 7:887-907. [PMID: 35910814 PMCID: PMC9306444 DOI: 10.1038/s41578-022-00460-x] [Citation(s) in RCA: 173] [Impact Index Per Article: 86.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/15/2022] [Indexed: 05/03/2023]
Abstract
Wearable devices provide an alternative pathway to clinical diagnostics by exploiting various physical, chemical and biological sensors to mine physiological (biophysical and/or biochemical) information in real time (preferably, continuously) and in a non-invasive or minimally invasive manner. These sensors can be worn in the form of glasses, jewellery, face masks, wristwatches, fitness bands, tattoo-like devices, bandages or other patches, and textiles. Wearables such as smartwatches have already proved their capability for the early detection and monitoring of the progression and treatment of various diseases, such as COVID-19 and Parkinson disease, through biophysical signals. Next-generation wearable sensors that enable the multimodal and/or multiplexed measurement of physical parameters and biochemical markers in real time and continuously could be a transformative technology for diagnostics, allowing for high-resolution and time-resolved historical recording of the health status of an individual. In this Review, we examine the building blocks of such wearable sensors, including the substrate materials, sensing mechanisms, power modules and decision-making units, by reflecting on the recent developments in the materials, engineering and data science of these components. Finally, we synthesize current trends in the field to provide predictions for the future trajectory of wearable sensors.
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Affiliation(s)
- H. Ceren Ates
- FIT Freiburg Center for Interactive Materials and Bioinspired Technology, University of Freiburg, Freiburg, Germany
- IMTEK – Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
| | - Peter Q. Nguyen
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
| | | | - Eden Morales-Narváez
- Biophotonic Nanosensors Laboratory, Centro de Investigaciones en Óptica, León, Mexico
| | - Firat Güder
- Department of Bioengineering, Imperial College London, London, UK
| | - James J. Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
- Institute of Medical Engineering & Science, Department of Biological Engineering, MIT, Cambridge, MA USA
- Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Can Dincer
- FIT Freiburg Center for Interactive Materials and Bioinspired Technology, University of Freiburg, Freiburg, Germany
- IMTEK – Department of Microsystems Engineering, University of Freiburg, Freiburg, Germany
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40
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Hsieh JC, Li Y, Wang H, Perz M, Tang Q, Tang KWK, Pyatnitskiy I, Reyes R, Ding H, Wang H. Design of hydrogel-based wearable EEG electrodes for medical applications. J Mater Chem B 2022; 10:7260-7280. [PMID: 35678148 DOI: 10.1039/d2tb00618a] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The electroencephalogram (EEG) is considered to be a promising method for studying brain disorders. Because of its non-invasive nature, subjects take a lower risk compared to some other invasive methods, while the systems record the brain signal. With the technological advancement of neural and material engineering, we are in the process of achieving continuous monitoring of neural activity through wearable EEG. In this article, we first give a brief introduction to EEG bands, circuits, wired/wireless EEG systems, and analysis algorithms. Then, we review the most recent advances in the interfaces used for EEG recordings, focusing on hydrogel-based EEG electrodes. Specifically, the advances for important figures of merit for EEG electrodes are reviewed. Finally, we summarize the potential medical application of wearable EEG systems.
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Affiliation(s)
- Ju-Chun Hsieh
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Yang Li
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C3J7, Canada
| | - Huiqian Wang
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Matt Perz
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Qiong Tang
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kai Wing Kevin Tang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Ilya Pyatnitskiy
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Raymond Reyes
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Hong Ding
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Huiliang Wang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
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41
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Developing cellulosic functional materials from multi-scale strategy and applications in flexible bioelectronic devices. Carbohydr Polym 2022; 283:119160. [DOI: 10.1016/j.carbpol.2022.119160] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/04/2022] [Accepted: 01/17/2022] [Indexed: 12/29/2022]
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42
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Xu X, Lian Z. Objective sleep assessments for healthy people in environmental research: A literature review. INDOOR AIR 2022; 32:e13034. [PMID: 35622713 DOI: 10.1111/ina.13034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/04/2022] [Accepted: 04/18/2022] [Indexed: 06/15/2023]
Abstract
To date, although many studies had focused on the impact of environmental factors on sleep, how to choose the proper assessment method for objective sleep quality was often ignored, especially for healthy subjects in bedroom environment. In order to provide methodological guidance for future research, this paper reviewed the assessments of objective sleep quality applied in environmental researches, compared them from the perspective of accuracy and interference, and statistically analyzed the impact of experimental type and subjects' information on method selection. The review results showed that, in contrast to polysomnography (PSG), the accuracy of actigraphy (ACT), respiratory monitoring-oxygen saturation monitoring (RM-OSM), and electrocardiograph (ECG) could reach up to 97%, 80.38%, and 79.95%, respectively. In terms of sleep staging, PSG and ECG performed the best, ACT the second, and RM-OSM the worst; as compared to single methods, mix methods were more accurate and better at sleep staging. PSG interfered with sleep a great deal, while ECG and ACT could be non-contact, and thus, the least interference with sleep was present. The type of experiment significantly influenced the choice of assessment method (p < 0.001), 85.3% of researchers chose PSG in laboratory study while 82.5% ACT in field study; moreover, PSG was often used in a relatively small number of young subjects, while ACT had a wide applicable population. In general, researchers need to pay more attention at selection of assessments in future studies, and this review can be used as a reliable reference for experimental design.
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Affiliation(s)
- Xinbo Xu
- School of Design, Shanghai Jiao Tong University, Shanghai, China
| | - Zhiwei Lian
- School of Design, Shanghai Jiao Tong University, Shanghai, China
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43
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Honda S, Hara H, Arie T, Akita S, Takei K. A wearable, flexible sensor for real-time, home monitoring of sleep apnea. iScience 2022; 25:104163. [PMID: 35434564 PMCID: PMC9010767 DOI: 10.1016/j.isci.2022.104163] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/11/2022] [Accepted: 03/23/2022] [Indexed: 11/02/2022] Open
Abstract
A flexible sensor that can be attached to the body to collect vital data wirelessly enables real-time human healthcare management. One potential application for home-use healthcare devices is monitoring of sleep conditions to diagnose sleep apnea syndrome. Such data are not readily gathered using conventional tools, owing to the bulk and cost of instrumentation. In order to monitor respiration at home, it is necessary to improve sensing performance and long-term stability of the sensors without sacrificing wearability and comfortability. To build a platform for wireless home-use respiration monitoring, this study develops a mask-borne flexible humidity sensor using ZnIn2S4 nanosheets as a humidity-sensitive material with high sensitivity and stability for more than 150 h. As proof-of-concept, long-term wireless respiration monitoring is demonstrated during sleep to identify symptoms of sleep apnea in wearers.
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Affiliation(s)
- Satoko Honda
- Department of Physics and Electronics, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan.,Department of Physics and Electronics, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
| | - Hyuga Hara
- Department of Physics and Electronics, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan.,Department of Physics and Electronics, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
| | - Takayuki Arie
- Department of Physics and Electronics, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan.,Department of Physics and Electronics, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
| | - Seiji Akita
- Department of Physics and Electronics, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan.,Department of Physics and Electronics, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
| | - Kuniharu Takei
- Department of Physics and Electronics, Osaka Prefecture University, Sakai, Osaka 599-8531, Japan.,Department of Physics and Electronics, Osaka Metropolitan University, Sakai, Osaka 599-8531, Japan
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44
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Radhakrishnan BL, Kirubakaran E, Jebadurai IJ, Selvakumar AI, Peter JD. Efficacy of Single-Channel EEG: A Propitious Approach for In-home Sleep Monitoring. Front Public Health 2022; 10:839838. [PMID: 35493356 PMCID: PMC9039057 DOI: 10.3389/fpubh.2022.839838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- B. L. Radhakrishnan
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India
- *Correspondence: B. L. Radhakrishnan
| | - E. Kirubakaran
- Department of Computer Science and Engineering, Grace College of Engineering, HWP Colony, Thoothukudi, India
| | - Immanuel Johnraja Jebadurai
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India
| | - A. Immanuel Selvakumar
- Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - J. Dinesh Peter
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India
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45
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Kwon K, Kwon S, Yeo WH. Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes. BIOSENSORS 2022; 12:bios12030155. [PMID: 35323425 PMCID: PMC8946692 DOI: 10.3390/bios12030155] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/09/2022] [Accepted: 02/28/2022] [Indexed: 05/13/2023]
Abstract
Sleep stage classification is an essential process of diagnosing sleep disorders and related diseases. Automatic sleep stage classification using machine learning has been widely studied due to its higher efficiency compared with manual scoring. Typically, a few polysomnography data are selected as input signals, and human experts label the corresponding sleep stages manually. However, the manual process includes human error and inconsistency in the scoring and stage classification. Here, we present a convolutional neural network (CNN)-based classification method that offers highly accurate, automatic sleep stage detection, validated by a public dataset and new data measured by wearable nanomembrane dry electrodes. First, our study makes a training and validation model using a public dataset with two brain signal and two eye signal channels. Then, we validate this model with a new dataset measured by a set of nanomembrane electrodes. The result of the automatic sleep stage classification shows that our CNN model with multi-taper spectrogram pre-processing achieved 88.85% training accuracy on the validation dataset and 81.52% prediction accuracy on our laboratory dataset. These results validate the reliability of our classification method on the standard polysomnography dataset and the transferability of our CNN model for other datasets measured with the wearable electrodes.
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Affiliation(s)
- Kangkyu Kwon
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA;
- IEN Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - Shinjae Kwon
- IEN Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA;
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Woon-Hong Yeo
- IEN Center for Human-Centric Interfaces and Engineering, Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA;
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Neural Engineering Center, Institute for Materials, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Correspondence: ; Tel.: +1-404-385-5710; Fax: +1-404-894-1658
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Ouyang F, Wang M, Liao M, Lan L, Liu X, Li H, Mo R, Shi L, Fan Y. Association between periodic limb movements during sleep and neuroimaging features of cerebral small vessel disease: A preliminary cross‐sectional study. J Sleep Res 2022; 31:e13573. [DOI: 10.1111/jsr.13573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/11/2022] [Accepted: 02/13/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Fubing Ouyang
- Department of Neurology National Key Clinical Department and Key Discipline of Neurology The First Affiliated Hospital Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases Sun Yat‐sen University Guangzhou China
| | - Meng Wang
- Department of Radiology The First Affiliated Hospital Sun Yat‐sen University Guangzhou China
| | - Mengshi Liao
- Department of Neurology National Key Clinical Department and Key Discipline of Neurology The First Affiliated Hospital Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases Sun Yat‐sen University Guangzhou China
| | - Linfan Lan
- Department of Neurology National Key Clinical Department and Key Discipline of Neurology The First Affiliated Hospital Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases Sun Yat‐sen University Guangzhou China
| | - Xiaolu Liu
- Department of Neurology National Key Clinical Department and Key Discipline of Neurology The First Affiliated Hospital Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases Sun Yat‐sen University Guangzhou China
| | - Hao Li
- Department of Neurology National Key Clinical Department and Key Discipline of Neurology The First Affiliated Hospital Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases Sun Yat‐sen University Guangzhou China
| | - Rong Mo
- Department of Neurology National Key Clinical Department and Key Discipline of Neurology The First Affiliated Hospital Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases Sun Yat‐sen University Guangzhou China
| | - Lin Shi
- Department of Imaging and Interventional Radiology The Chinese University of Hong Kong Hong Kong China
| | - Yuhua Fan
- Department of Neurology National Key Clinical Department and Key Discipline of Neurology The First Affiliated Hospital Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases Sun Yat‐sen University Guangzhou China
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Ghorbani S, Golkashani HA, Chee NIYN, Teo TB, Dicom AR, Yilmaz G, Leong RLF, Ong JL, Chee MWL. Multi-Night at-Home Evaluation of Improved Sleep Detection and Classification with a Memory-Enhanced Consumer Sleep Tracker. Nat Sci Sleep 2022; 14:645-660. [PMID: 35444483 PMCID: PMC9015046 DOI: 10.2147/nss.s359789] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/31/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE To evaluate the benefits of applying an improved sleep detection and staging algorithm on minimally processed multi-sensor wearable data collected from older generation hardware. PATIENTS AND METHODS 58 healthy, East Asian adults aged 23-69 years (M = 37.10, SD = 13.03, 32 males), each underwent 3 nights of PSG at home, wearing 2nd Generation Oura Rings equipped with additional memory to store raw data from accelerometer, infra-red photoplethysmography and temperature sensors. 2-stage and 4-stage sleep classifications using a new machine-learning algorithm (Gen3) trained on a diverse and independent dataset were compared to the existing consumer algorithm (Gen2) for whole-night and epoch-by-epoch metrics. RESULTS Gen 3 outperformed its predecessor with a mean (SD) accuracy of 92.6% (0.04), sensitivity of 94.9% (0.03), and specificity of 78.5% (0.11); corresponding to a 3%, 2.8% and 6.2% improvement from Gen2 across the three nights, with Cohen's d values >0.39, t values >2.69, and p values <0.01. Notably, Gen 3 showed robust performance comparable to PSG in its assessment of sleep latency, light sleep, rapid eye movement (REM), and wake after sleep onset (WASO) duration. Participants <40 years of age benefited more from the upgrade with less measurement bias for total sleep time (TST), WASO, light sleep and sleep efficiency compared to those ≥40 years. Males showed greater improvements on TST and REM sleep measurement bias compared to females, while females benefitted more for deep sleep measures compared to males. CONCLUSION These results affirm the benefits of applying machine learning and a diverse training dataset to improve sleep measurement of a consumer wearable device. Importantly, collecting raw data with appropriate hardware allows for future advancements in algorithm development or sleep physiology to be retrospectively applied to enhance the value of longitudinal sleep studies.
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Affiliation(s)
- Shohreh Ghorbani
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Hosein Aghayan Golkashani
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Nicholas I Y N Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Teck Boon Teo
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Andrew Roshan Dicom
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Gizem Yilmaz
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ruth L F Leong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ju Lynn Ong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Michael W L Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Wang X, Wang P, Liu C, Qin S, Wan Q, Luo S, Wu W. Acupuncture for hypertension with insomnia: Study protocol for a randomized, sham-controlled, subject-and-assessor-blinded trial. Front Psychiatry 2022; 13:1087706. [PMID: 36620662 PMCID: PMC9813511 DOI: 10.3389/fpsyt.2022.1087706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Previous studies show that insomnia and hypertension are closely related. Currently, intervention for hypertension with insomnia has become a research hotspot. Acupuncture, as a representative non-pharmaceutical therapy of traditional Chinese medicine (TCM), has been widely used in improving insomnia and hypertension. However, there are few clinical studies on acupuncture for hypertension with insomnia. METHODS A single-center, subject-and-assessor-blind, randomized, sham-controlled trial has been designed for a study to be conducted in Jiangsu Province Hospital of Chinese Medicine. Sixty eligible patients will be randomly assigned to the treatment group and the control group in a 1:1 ratio. The treatment group will receive acupuncture treatment, while the control group will receive sham acupuncture treatment. Both groups will be treated three times per week for 4 weeks. Data will be collected at baseline and after 4 weeks of treatment and analyzed by using SPSS 25.0. The primary outcome measures are sleep parameters of portable polysomnography before and after treatment. Secondary outcomes are Pittsburgh Sleep Quality Index, Insomnia Severity Index, home blood pressure, and heart rate variability. DISCUSSION This study aims to evaluate the efficacy of acupuncture using the portable polysomnography combined with sleep scales, and analyze heart rate variability to preliminarily explore the underlying mechanism of acupuncture on hypertension with insomnia. The trail, if proven to be effective, will provide strong scientific evidence to support acupuncture is effective to manage patients for hypertension with insomnia. CLINICAL TRIAL REGISTRATION ChiCTR2200059161.
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Affiliation(s)
- Xiaoqiu Wang
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Pei Wang
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Chengyong Liu
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Shan Qin
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Qingyun Wan
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Shuting Luo
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wenzhong Wu
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
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Serrano Alarcón Á, Martínez Madrid N, Seepold R. A Minimum Set of Physiological Parameters to Diagnose Obstructive Sleep Apnea Syndrome Using Non-Invasive Portable Monitors. A Systematic Review. Life (Basel) 2021; 11:1249. [PMID: 34833126 PMCID: PMC8623368 DOI: 10.3390/life11111249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 11/17/2022] Open
Abstract
Introduction. Despite its high accuracy, polysomnography (PSG) has several drawbacks for diagnosing obstructive sleep apnea (OSA). Consequently, multiple portable monitors (PMs) have been proposed. Objective. This systematic review aims to investigate the current literature to analyze the sets of physiological parameters captured by a PM to select the minimum number of such physiological signals while maintaining accurate results in OSA detection. Methods. Inclusion and exclusion criteria for the selection of publications were established prior to the search. The evaluation of the publications was made based on one central question and several specific questions. Results. The abilities to detect hypopneas, sleep time, or awakenings were some of the features studied to investigate the full functionality of the PMs to select the most relevant set of physiological signals. Based on the physiological parameters collected (one to six), the PMs were classified into sets according to the level of evidence. The advantages and the disadvantages of each possible set of signals were explained by answering the research questions proposed in the methods. Conclusions. The minimum number of physiological signals detected by PMs for the detection of OSA depends mainly on the purpose and context of the sleep study. The set of three physiological signals showed the best results in the detection of OSA.
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Affiliation(s)
- Ángel Serrano Alarcón
- School of Informatics, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany;
| | - Natividad Martínez Madrid
- School of Informatics, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany;
- Institute of Digital Medicine, I.M. Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya st., 119435 Moscow, Russian Federation;
| | - Ralf Seepold
- Institute of Digital Medicine, I.M. Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya st., 119435 Moscow, Russian Federation;
- HTWG Konstanz, Department of Computer Science, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
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