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Kazemi K, Abiri A, Zhou Y, Rahmani A, Khayat RN, Liljeberg P, Khine M. Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns. Comput Biol Med 2024; 179:108679. [PMID: 39033682 DOI: 10.1016/j.compbiomed.2024.108679] [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: 09/12/2023] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 07/23/2024]
Abstract
Sleep staging is a crucial tool for diagnosing and monitoring sleep disorders, but the standard clinical approach using polysomnography (PSG) in a sleep lab is time-consuming, expensive, uncomfortable, and limited to a single night. Advancements in sensor technology have enabled home sleep monitoring, but existing devices still lack sufficient accuracy to inform clinical decisions. To address this challenge, we propose a deep learning architecture that combines a convolutional neural network and bidirectional long short-term memory to accurately classify sleep stages. By supplementing photoplethysmography (PPG) signals with respiratory sensor inputs, we demonstrated significant improvements in prediction accuracy and Cohen's kappa (k) for 2- (92.7 %; k = 0.768), 3- (80.2 %; k = 0.714), 4- (76.8 %, k = 0.550), and 5-stage (76.7 %, k = 0.616) sleep classification using raw data. This relatively translatable approach, with a less intensive AI model and leveraging only a few, inexpensive sensors, shows promise in accurately staging sleep. This has potential for diagnosing and managing sleep disorders in a more accessible and practical manner, possibly even at home.
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Affiliation(s)
| | - Arash Abiri
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, United States
| | - Yongxiao Zhou
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, United States
| | - Amir Rahmani
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States; School of Nursing, University of California, Irvine, Irvine, CA, United States
| | - Rami N Khayat
- Division of Pulmonary and Critical Care Medicine, The UCI Comprehensive Sleep Center, University of California. Irvine, Newport Beach, CA, United States
| | | | - Michelle Khine
- Department of Biomedical Engineering, University of California Irvine, Irvine, CA, United States.
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2
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Jaworski D, Park EJ. Nonlinear Heart Rate Variability Analysis for Sleep Stage Classification Using Integration of Ballistocardiogram and Apple Watch. Nat Sci Sleep 2024; 16:1075-1090. [PMID: 39081512 PMCID: PMC11288323 DOI: 10.2147/nss.s464944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 06/28/2024] [Indexed: 08/02/2024] Open
Abstract
Purpose Wearable or non-contact, non-intrusive devices present a practical alternative to traditional polysomnography (PSG) for daily assessment of sleep quality. Physiological signals have been known to be nonlinear and nonstationary as the body adapts to states of rest or activity. By integrating more sophisticated nonlinear methodologies, the accuracy of sleep stage identification using such devices can be improved. This advancement enables individuals to monitor and adjust their sleep patterns more effectively without visiting sleep clinics. Patients and Methods Six participants slept for three cycles of at least three hours each, wearing PSG as a reference, along with an Apple Watch, an actigraphy device, and a ballistocardiography (BCG) bed sensor. The physiological signals were processed with nonlinear methods and trained with a long short-term memory (LSTM) model to classify sleep stages. Nonlinear methods, such as return maps with advanced techniques to analyze the shape and asymmetry in physiological signals, were used to relate these signals to the autonomic nervous system (ANS). The changing dynamics of cardiac signals in restful or active states, regulated by the ANS, were associated with sleep stages and quality, which were measurable. Results Approximately 73% agreement was obtained by comparing the combination of the BCG and Apple Watch signals against a PSG reference system to classify rapid eye movement (REM) and non-REM sleep stages. Conclusion Utilizing nonlinear methods to evaluate cardiac dynamics showed an improved sleep quality detection with the non-intrusive devices in this study. A system of non-intrusive devices can provide a comprehensive outlook on health by regularly measuring sleeping patterns and quality over time, offering a relatively accessible method for participants. Additionally, a non-intrusive system can be integrated into a user's or clinic's bedroom environment to measure and evaluate sleep quality without negatively impacting sleep. Devices placed around the bedroom could measure user vitals over longer periods with minimal interaction from the user, representing their natural sleeping trends for more accurate health and sleep disorder diagnosis.
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Affiliation(s)
- Dominic Jaworski
- Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, V3T 0A3, Canada
- WearTech Labs, Simon Fraser University, Surrey, BC, V3V 0C6, Canada
| | - Edward J Park
- Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, V3T 0A3, Canada
- WearTech Labs, Simon Fraser University, Surrey, BC, V3V 0C6, Canada
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Silva FB, Uribe LFS, Cepeda FX, Alquati VFS, Guimarães JPS, Silva YGA, Santos OLD, de Oliveira AA, de Aguiar GHM, Andersen ML, Tufik S, Lee W, Li LT, Penatti OA. Sleep staging algorithm based on smartwatch sensors for healthy and sleep apnea populations. Sleep Med 2024; 119:535-548. [PMID: 38810479 DOI: 10.1016/j.sleep.2024.05.033] [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: 03/26/2024] [Revised: 05/06/2024] [Accepted: 05/14/2024] [Indexed: 05/31/2024]
Abstract
OBJECTIVE Sleep stages can provide valuable insights into an individual's sleep quality. By leveraging movement and heart rate data collected by modern smartwatches, it is possible to enable the sleep staging feature and enhance users' understanding about their sleep and health conditions. METHOD In this paper, we present and validate a recurrent neural network based model with 23 input features extracted from accelerometer and photoplethysmography sensors data for both healthy and sleep apnea populations. We designed a lightweight and fast solution to enable the prediction of sleep stages for each 30-s epoch. This solution was developed using a large dataset of 1522 night recordings collected from a highly heterogeneous population and different versions of Samsung smartwatch. RESULTS In the classification of four sleep stages (wake, light, deep, and rapid eye movements sleep), the proposed solution achieved 71.6 % of balanced accuracy and a Cohen's kappa of 0.56 in a test set with 586 recordings. CONCLUSION The results presented in this paper validate our proposal as a competitive wearable solution for sleep staging. Additionally, the use of a large and diverse data set contributes to the robustness of our solution, and corroborates the validation of algorithm's performance. Some additional analysis performed for healthy and sleep apnea population demonstrated that algorithm's performance has low correlation with demographic variables.
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Affiliation(s)
- Fernanda B Silva
- Samsung R&D Institute Brazil (SRBR), Campinas, SP, 13097-160, Brazil.
| | - Luisa F S Uribe
- Samsung R&D Institute Brazil (SRBR), Campinas, SP, 13097-160, Brazil.
| | - Felipe X Cepeda
- Samsung R&D Institute Brazil (SRBR), Campinas, SP, 13097-160, Brazil
| | - Vitor F S Alquati
- Samsung R&D Institute Brazil (SRBR), Campinas, SP, 13097-160, Brazil
| | | | - Yuri G A Silva
- Samsung R&D Institute Brazil (SRBR), Campinas, SP, 13097-160, Brazil
| | | | | | | | - Monica L Andersen
- Sleep Institute, São Paulo, SP, 04020-060, Brazil; Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, SP, 04724-000, Brazil
| | - Sergio Tufik
- Sleep Institute, São Paulo, SP, 04020-060, Brazil; Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, SP, 04724-000, Brazil
| | - Wonkyu Lee
- Samsung Electronics, Suwon, 16677, Republic of Korea
| | - Lin Tzy Li
- Samsung R&D Institute Brazil (SRBR), Campinas, SP, 13097-160, Brazil
| | - Otávio A Penatti
- Samsung R&D Institute Brazil (SRBR), Campinas, SP, 13097-160, Brazil
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4
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Wulterkens BM, Den Teuling NGP, Hermans LWA, Asin J, Duis N, Overeem S, Fonseca P, van Gilst MM. Multi-night home assessment of sleep structure in OSA with and without insomnia. Sleep Med 2024; 117:152-161. [PMID: 38547592 DOI: 10.1016/j.sleep.2024.03.031] [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: 01/26/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 04/16/2024]
Abstract
OBJECTIVE To explore sleep structure in participants with obstructive sleep apnea (OSA) and comorbid insomnia (COMISA) and participants with OSA without insomnia (OSA-only) using both single-night polysomnography and multi-night wrist-worn photoplethysmography/accelerometry. METHODS Multi-night 4-class sleep-staging was performed with a validated algorithm based on actigraphy and heart rate variability, in 67 COMISA (23 women, median age: 51 years) and 50 OSA-only (15 women, median age: 51) participants. Sleep statistics were compared using linear regression models and mixed-effects models. Multi-night variability was explored using a clustering approach and between- and within-participant analysis. RESULTS Polysomnographic parameters showed no significant group differences. Multi-night measurements, during 13.4 ± 5.2 nights per subject, demonstrated a longer sleep onset latency and lower sleep efficiency for the COMISA group. Detailed analysis of wake parameters revealed longer mean durations of awakenings in COMISA, as well as higher numbers of awakenings lasting 5 min and longer (WKN≥5min) and longer wake after sleep onset containing only awakenings of 5 min or longer. Within-participant variance was significantly larger in COMISA for sleep onset latency, sleep efficiency, mean duration of awakenings and WKN≥5min. Unsupervised clustering uncovered three clusters; participants with consistently high values for at least one of the wake parameters, participants with consistently low values, and participants displaying higher variability. CONCLUSION Patients with COMISA more often showed extended, and more variable periods of wakefulness. These observations were not discernible using single night polysomnography, highlighting the relevance of multi-night measurements to assess characteristics indicative for insomnia.
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Affiliation(s)
- Bernice M Wulterkens
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Philips Sleep and Respiratory Care, Eindhoven, the Netherlands.
| | | | - Lieke W A Hermans
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Jerryll Asin
- Center for Sleep Medicine, Amphia Hospital, Breda, the Netherlands
| | - Nanny Duis
- Center for Sleep Medicine, Amphia Hospital, Breda, the Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Philips Sleep and Respiratory Care, Eindhoven, the Netherlands
| | - Merel M van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands
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Ma YJX, Zschocke J, Glos M, Kluge M, Penzel T, Kantelhardt JW, Bartsch RP. Sleep-stage dependence and co-existence of cardio-respiratory coordination and phase synchronization. CHAOS (WOODBURY, N.Y.) 2024; 34:043118. [PMID: 38572945 DOI: 10.1063/5.0177552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024]
Abstract
Interactions between the cardiac and respiratory systems play a pivotal role in physiological functioning. Nonetheless, the intricacies of cardio-respiratory couplings, such as cardio-respiratory phase synchronization (CRPS) and cardio-respiratory coordination (CRC), remain elusive, and an automated algorithm for CRC detection is lacking. This paper introduces an automated CRC detection algorithm, which allowed us to conduct a comprehensive comparison of CRPS and CRC during sleep for the first time using an extensive database. We found that CRPS is more sensitive to sleep-stage transitions, and intriguingly, there is a negative correlation between the degree of CRPS and CRC when fluctuations in breathing frequency are high. This comparative analysis holds promise in assisting researchers in gaining deeper insights into the mechanics of and distinctions between these two physiological phenomena. Additionally, the automated algorithms we devised have the potential to offer valuable insights into the clinical applications of CRC and CRPS.
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Affiliation(s)
- Yaopeng J X Ma
- Department of Physics, Bar-Ilan University, 5290002 Ramat Gan, Israel
| | - Johannes Zschocke
- Institute of Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Center for Health Sciences, Martin-Luther-University Halle-Wittenberg, 06097 Halle (Saale), Germany
- Institute of Physics, Martin-Luther University Halle-Wittenberg, 06099 Halle (Saale), Germany
| | - Martin Glos
- Interdisciplinary Center for Sleep Medicine, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Maria Kluge
- Interdisciplinary Center for Sleep Medicine, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Thomas Penzel
- Interdisciplinary Center for Sleep Medicine, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Jan W Kantelhardt
- Institute of Physics, Martin-Luther University Halle-Wittenberg, 06099 Halle (Saale), Germany
| | - Ronny P Bartsch
- Department of Physics, Bar-Ilan University, 5290002 Ramat Gan, Israel
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6
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Hermans L, van Meulen F, Anderer P, Ross M, Cerny A, van Gilst M, Overeem S, Fonseca P. Performance of cardiorespiratory-based sleep staging in patients using beta blockers. J Clin Sleep Med 2024; 20:575-581. [PMID: 38063156 PMCID: PMC10985295 DOI: 10.5664/jcsm.10938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 04/04/2024]
Abstract
STUDY OBJECTIVES Automatic sleep staging based on cardiorespiratory signals from home sleep monitoring devices holds great clinical potential. Using state-of-the-art machine learning, promising performance has been reached in patients with sleep disorders. However, it is unknown whether performance would hold in individuals with potentially altered autonomic physiology, for example under the influence of medication. Here, we assess an existing sleep staging algorithm in patients with sleep disorders with and without the use of beta blockers. METHODS We analyzed a retrospective dataset of sleep recordings of 57 patients with sleep disorders using beta blockers and 57 age-matched patients with sleep disorders not using beta blockers. Sleep stages were automatically scored based on electrocardiography and respiratory effort from a thoracic belt, using a previously developed machine-learning algorithm (CReSS algorithm). For both patient groups, sleep stages classified by the model were compared to gold standard manual polysomnography scoring using epoch-by-epoch agreement. Additionally, for both groups, overall sleep parameters were calculated and compared between the two scoring methods. RESULTS Substantial agreement was achieved for four-class sleep staging in both patient groups (beta blockers: kappa = 0.635, accuracy = 78.1%; controls: kappa = 0.660, accuracy = 78.8%). No statistical difference in epoch-by-epoch agreement was found between the two groups. Additionally, the groups did not differ on agreement of derived sleep parameters. CONCLUSIONS We showed that the performance of the CReSS algorithm is not deteriorated in patients using beta blockers. Results do not indicate a fundamental limitation in leveraging autonomic characteristics to obtain a surrogate measure of sleep in this clinically relevant population. CITATION Hermans L, van Meulen F, Anderer P, et al. Performance of cardiorespiratory-based sleep staging in patients using beta blockers. J Clin Sleep Med. 2024;20(4):575-581.
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Affiliation(s)
- Lieke Hermans
- Philips Research, Eindhoven, The Netherlands
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
| | - Fokke van Meulen
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Peter Anderer
- Philips Sleep and Respiratory Care, Vienna, Austria
- The Siesta Group Schlafanalyse GmbH, Vienna, Austria
| | - Marco Ross
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
- Philips Sleep and Respiratory Care, Vienna, Austria
- The Siesta Group Schlafanalyse GmbH, Vienna, Austria
| | | | - Merel van Gilst
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Pedro Fonseca
- Philips Research, Eindhoven, The Netherlands
- Department of Electrical Engineering, TU/e Eindhoven, Eindhoven, The Netherlands
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Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Med Rev 2024; 74:101897. [PMID: 38306788 DOI: 10.1016/j.smrv.2024.101897] [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: 10/02/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
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Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuqi Chen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Guo
- 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
| | - Yidan Dai
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Yiming Wang
- 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; Department of Otolaryngology, The Sixth 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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Höhn C, Hahn MA, Lendner JD, Hoedlmoser K. Spectral Slope and Lempel-Ziv Complexity as Robust Markers of Brain States during Sleep and Wakefulness. eNeuro 2024; 11:ENEURO.0259-23.2024. [PMID: 38471778 PMCID: PMC10978822 DOI: 10.1523/eneuro.0259-23.2024] [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: 07/14/2023] [Revised: 01/22/2024] [Accepted: 02/09/2024] [Indexed: 03/14/2024] Open
Abstract
Nonoscillatory measures of brain activity such as the spectral slope and Lempel-Ziv complexity are affected by many neurological disorders and modulated by sleep. A multitude of frequency ranges, particularly a broadband (encompassing the full spectrum) and a narrowband approach, have been used especially for estimating the spectral slope. However, the effects of choosing different frequency ranges have not yet been explored in detail. Here, we evaluated the impact of sleep stage and task engagement (resting, attention, and memory) on slope and complexity in a narrowband (30-45 Hz) and broadband (1-45 Hz) frequency range in 28 healthy male human subjects (21.54 ± 1.90 years) using a within-subject design over 2 weeks with three recording nights and days per subject. We strived to determine how different brain states and frequency ranges affect slope and complexity and how the two measures perform in comparison. In the broadband range, the slope steepened, and complexity decreased continuously from wakefulness to N3 sleep. REM sleep, however, was best discriminated by the narrowband slope. Importantly, slope and complexity also differed between tasks during wakefulness. While narrowband complexity decreased with task engagement, the slope flattened in both frequency ranges. Interestingly, only the narrowband slope was positively correlated with task performance. Our results show that slope and complexity are sensitive indices of brain state variations during wakefulness and sleep. However, the spectral slope yields more information and could be used for a greater variety of research questions than Lempel-Ziv complexity, especially when a narrowband frequency range is used.
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Affiliation(s)
- Christopher Höhn
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, 5020 Salzburg, Austria
| | - Michael A Hahn
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany
| | - Janna D Lendner
- Hertie-Institute for Clinical Brain Research, University Medical Center Tübingen, 72076 Tübingen, Germany
- Department of Anesthesiology and Intensive Care Medicine, University Medical Center Tübingen, 72076 Tübingen, Germany
| | - Kerstin Hoedlmoser
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, University of Salzburg, 5020 Salzburg, Austria
- Centre for Cognitive Neuroscience Salzburg (CCNS), University of Salzburg, 5020 Salzburg, Austria
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Topalidis PI, Baron S, Heib DPJ, Eigl ES, Hinterberger A, Schabus M. From Pulses to Sleep Stages: Towards Optimized Sleep Classification Using Heart-Rate Variability. SENSORS (BASEL, SWITZERLAND) 2023; 23:9077. [PMID: 38005466 PMCID: PMC10674316 DOI: 10.3390/s23229077] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 10/31/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023]
Abstract
More and more people quantify their sleep using wearables and are becoming obsessed in their pursuit of optimal sleep ("orthosomnia"). However, it is criticized that many of these wearables are giving inaccurate feedback and can even lead to negative daytime consequences. Acknowledging these facts, we here optimize our previously suggested sleep classification procedure in a new sample of 136 self-reported poor sleepers to minimize erroneous classification during ambulatory sleep sensing. Firstly, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of the overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., "light sleep"). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. The results reveal a high overall accuracy for the loss function in ECG (86.3 %, κ = 0.79), as well as the H10 (84.4%, κ = 0.76), and VS (84.2%, κ = 0.75) sensors, with improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication, which can be considered a prerequisite in older individuals with or without common disorders. Further improving and validating automatic sleep stage classification algorithms based on signals from affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice.
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Affiliation(s)
- Pavlos I. Topalidis
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria; (P.I.T.); (D.P.J.H.); (E.-S.E.)
| | - Sebastian Baron
- Department of Mathematics, Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
- Department of Artificial Intelligence and Human Interfaces (AIHI), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
| | - Dominik P. J. Heib
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria; (P.I.T.); (D.P.J.H.); (E.-S.E.)
- Institut Proschlaf, 5020 Salzburg, Austria
| | - Esther-Sevil Eigl
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria; (P.I.T.); (D.P.J.H.); (E.-S.E.)
| | - Alexandra Hinterberger
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria; (P.I.T.); (D.P.J.H.); (E.-S.E.)
| | - Manuel Schabus
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology, Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria; (P.I.T.); (D.P.J.H.); (E.-S.E.)
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10
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Abbasi SF, Abbas A, Ahmad I, Alshehri MS, Almakdi S, Ghadi YY, Ahmad J. Automatic neonatal sleep stage classification: A comparative study. Heliyon 2023; 9:e22195. [PMID: 38058619 PMCID: PMC10695968 DOI: 10.1016/j.heliyon.2023.e22195] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/21/2023] [Accepted: 11/06/2023] [Indexed: 12/08/2023] Open
Abstract
Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study.
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Affiliation(s)
- Saadullah Farooq Abbasi
- Department of Electronic, Electrical and System Engineering, University of Birmingham, Birmingham, United Kingdom
| | - Awais Abbas
- Department of Electronic, Electrical and System Engineering, University of Birmingham, Birmingham, United Kingdom
| | - Iftikhar Ahmad
- James Watt School of Engineering, University of Glasgow, United Kingdom
| | - Mohammed S. Alshehri
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Sultan Almakdi
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
| | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates
| | - Jawad Ahmad
- School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK
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11
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Morokuma S, Hayashi T, Kanegae M, Mizukami Y, Asano S, Kimura I, Tateizumi Y, Ueno H, Ikeda S, Niizeki K. Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders. Sci Rep 2023; 13:17730. [PMID: 37853134 PMCID: PMC10584883 DOI: 10.1038/s41598-023-45020-7] [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: 06/16/2023] [Accepted: 10/14/2023] [Indexed: 10/20/2023] Open
Abstract
Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespiratory and body movement data. Overnight polysomnography (PSG) data from 123 participants (age: 19-82 years) with suspected sleep disorders were analyzed. Multivariate time series data, including heart rate, respiratory rate, cardiorespiratory coupling, and body movement frequency, were input into a bidirectional long short-term memory (biLSTM) network model to train and predict five-class sleep stages. The trained model's performance was evaluated using balanced accuracy, Cohen's κ coefficient, and F1 scores on an epoch-per-epoch basis and compared with the ground truth using the leave-one-out cross-validation scheme. The model achieved an accuracy of 71.2 ± 5.8%, Cohen's κ of 0.425 ± 0.115, and an F1 score of 0.650 ± 0.083 across all sleep stages, and all metrics were negatively correlated with the apnea-hypopnea index, as well as age, but positively correlated with sleep efficiency. Moreover, the model performance varied for each sleep stage, with the highest F1 score observed for N2 and the lowest for N3. Regression and Bland-Altman analyses between sleep parameters of interest derived from deep learning and PSG showed substantial correlations (r = 0.33-0.60) with low bias. The findings demonstrate the efficacy of the biLSTM deep learning model in accurately classifying sleep stages and in estimating sleep parameters for sleep structure analysis using a reduced set of physiological parameters. The current model without using EEG information may expand the application of unobtrusive in-home monitoring to clinically assess the prevalence of sleep disorders outside of a sleep laboratory.
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Affiliation(s)
- Seiichi Morokuma
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
| | | | | | | | | | | | - Yuji Tateizumi
- Department of Electrical Engineering, National Institute of Technology, Tokyo College, Tokyo, Japan
| | - Hitoshi Ueno
- Tokyo Information Design Professional University, Tokyo, Japan
| | - Subaru Ikeda
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kyuichi Niizeki
- Department of Biosystems Engineering, Graduate School of Science and Engineering, Yamagata University (Emeritus), Yonezawa, Japan
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12
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Liu PK, Ting N, Chiu HC, Lin YC, Liu YT, Ku BW, Lee PL. Validation of photoplethysmography- and acceleration-based sleep staging in a community sample: comparison with polysomnography and Actiwatch. J Clin Sleep Med 2023; 19:1797-1810. [PMID: 37338335 PMCID: PMC10545987 DOI: 10.5664/jcsm.10690] [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: 02/23/2023] [Revised: 06/15/2023] [Accepted: 06/15/2023] [Indexed: 06/21/2023]
Abstract
STUDY OBJECTIVES Although wrist-worn consumer wearables are widely used for home sleep monitoring, few have been validated. It is unclear whether consumer wearables could be an alternative to Actiwatch. This study aimed to establish and validate an automatic sleep staging system (ASSS) utilizing photoplethysmography and acceleration data collected from a wrist-worn wearable device. METHODS Seventy-five participants from a community population underwent overnight polysomnography (PSG) while wearing a smartwatch (MT2511) and Actiwatch Spectrum Plus (Philips Respironics, Inc; Murrysville, PA, USA). Photoplethysmography and acceleration data collected from the smartwatches were utilized to build a 4-stage (wake, light sleep, deep sleep, and rapid eye movement [REM] sleep) classifier, which was validated against PSG. The performance of the sleep/wake classifier was compared with Actiwatch. All analyses were conducted separately for participants with PSG sleep efficiency (SE) ≥ 80% and SE < 80%. RESULTS The 4-stage classifier and PSG showed fair overall epoch-by-epoch agreement (kappa, 0.55; 95% confidence interval, 0.52 to 0.57). The deep sleep and REM times were comparable between ASSS and PSG, while ASSS underestimated the wake time and overestimated the light sleep time among participants with SE < 80%. Moreover, ASSS underestimated sleep-onset latency and wake after sleep onset and overestimated total sleep time and SE among participants with SE < 80%, while all were comparable among participants with SE ≥ 80%. The biases were smaller for ASSS than for Actiwatch. CONCLUSIONS Our photoplethysmography- and acceleration-based ASSS was reliable for participants with SE ≥ 80% and had a smaller bias than Actiwatch among those with SE < 80%. Thus, ASSS may be a promising alternative to Actiwatch. CLINICAL TRIAL REGISTRATION Registry: ClinicalTrials.gov; Name: Validation of Sleep Healthcare System; URL: https://clinicaltrials.gov/study/NCT04252482; Identifier: NCT04252482. CITATION Liu P-K, Ting N, Chiu H-C, et al. Validation of photoplethysmography- and acceleration-based sleep staging in a community sample: comparison with polysomnography and Actiwatch. J Clin Sleep Med. 2023;19(10):1797-1810.
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Affiliation(s)
- Po-Kang Liu
- Department of Multimedia Technology Development, MediaTek, Inc., Hsinchu, Taiwan
| | - Nettie Ting
- Department of Multimedia Technology Development, MediaTek, Inc., Hsinchu, Taiwan
| | - Hung-Chih Chiu
- Department of Multimedia Technology Development, MediaTek, Inc., Hsinchu, Taiwan
| | - Yu-Cheng Lin
- Department of Multimedia Technology Development, MediaTek, Inc., Hsinchu, Taiwan
| | - Yu-Ting Liu
- Department of Multimedia Technology Development, MediaTek, Inc., Hsinchu, Taiwan
| | - Bo-Wen Ku
- Department of Multimedia Technology Development, MediaTek, Inc., Hsinchu, Taiwan
| | - Pei-Lin Lee
- Center of Sleep Disorder, National Taiwan University Hospital, Taipei, Taiwan
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- Center for Electronics Technology Integration, National Taiwan University, Taipei, Taiwan
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13
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Parlato S, Centracchio J, Esposito D, Bifulco P, Andreozzi E. ECG-Free Heartbeat Detection in Seismocardiography and Gyrocardiography Signals Provides Acceptable Heart Rate Variability Indices in Healthy and Pathological Subjects. SENSORS (BASEL, SWITZERLAND) 2023; 23:8114. [PMID: 37836942 PMCID: PMC10575135 DOI: 10.3390/s23198114] [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: 08/22/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.
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Affiliation(s)
| | - Jessica Centracchio
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (S.P.); (D.E.); (P.B.)
| | | | | | - Emilio Andreozzi
- Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy; (S.P.); (D.E.); (P.B.)
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14
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Ross M, Fonseca P, Overeem S, Vasko R, Cerny A, Shaw E, Anderer P. Autonomic arousal detection and cardio-respiratory sleep staging improve the accuracy of home sleep apnea tests. Front Physiol 2023; 14:1254679. [PMID: 37693002 PMCID: PMC10484584 DOI: 10.3389/fphys.2023.1254679] [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: 07/07/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023] Open
Abstract
Introduction: The apnea-hypopnea index (AHI), defined as the number of apneas and hypopneas per hour of sleep, is still used as an important index to assess sleep disordered breathing (SDB) severity, where hypopneas are confirmed by the presence of an oxygen desaturation or an arousal. Ambulatory polygraphy without neurological signals, often referred to as home sleep apnea testing (HSAT), can potentially underestimate the severity of sleep disordered breathing (SDB) as sleep and arousals are not assessed. We aim to improve the diagnostic accuracy of HSATs by extracting surrogate sleep and arousal information derived from autonomic nervous system activity with artificial intelligence. Methods: We used polysomnographic (PSG) recordings from 245 subjects (148 with simultaneously recorded HSATs) to develop and validate a new algorithm to detect autonomic arousals using artificial intelligence. A clinically validated auto-scoring algorithm (Somnolyzer) scored respiratory events, cortical arousals, and sleep stages in PSGs, and provided respiratory events and sleep stages from cardio-respiratory signals in HSATs. In a four-fold cross validation of the newly developed algorithm, we evaluated the accuracy of the estimated arousal index and HSAT-derived surrogates for the AHI. Results: The agreement between the autonomic and cortical arousal index was moderate to good with an intraclass correlation coefficient of 0.73. When using thresholds of 5, 15, and 30 to categorize SDB into none, mild, moderate, and severe, the addition of sleep and arousal information significantly improved the classification accuracy from 70.2% (Cohen's κ = 0.58) to 80.4% (κ = 0.72), with a significant reduction of patients where the severity category was underestimated from 18.8% to 7.3%. Discussion: Extracting sleep and arousal information from autonomic nervous system activity can improve the diagnostic accuracy of HSATs by significantly reducing the probability of underestimating SDB severity without compromising specificity.
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Affiliation(s)
- Marco Ross
- Philips Sleep and Respiratory Care, Vienna, Austria
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Philips Research, Eindhoven, Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, Netherlands
| | - Ray Vasko
- Philips Sleep and Respiratory Care, Pittsburgh, PA, United States
| | | | - Edmund Shaw
- Philips Sleep and Respiratory Care, Pittsburgh, PA, United States
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15
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Yoon H, Choi SH. Technologies for sleep monitoring at home: wearables and nearables. Biomed Eng Lett 2023; 13:313-327. [PMID: 37519880 PMCID: PMC10382403 DOI: 10.1007/s13534-023-00305-8] [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: 03/31/2023] [Revised: 06/17/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
Sleep is an essential part of our lives and daily sleep monitoring is crucial for maintaining good health and well-being. Traditionally, the gold standard method for sleep monitoring is polysomnography using various sensors attached to the body; however, it is limited with regards to long-term sleep monitoring in a home environment. Recent advancements in wearable and nearable technology have made it possible to monitor sleep at home. In this review paper, the technologies that are currently available for sleep stages and sleep disorder monitoring at home are reviewed using wearable and nearable devices. Wearables are devices that are worn on the body, while nearables are placed near the body. These devices can accurately monitor sleep stages and sleep disorder in a home environment. In this study, the benefits and limitations of each technology are discussed, along with their potential to improve sleep quality.
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Affiliation(s)
- Heenam Yoon
- Department of Human-Centered Artificial Intelligence, Sangmyung University, Seoul, 03016 Korea
| | - Sang Ho Choi
- School of Computer and Information Engineering, Kwangwoon University, Seoul, 01897 Korea
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16
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Gaiduk M, Serrano Alarcón Á, Seepold R, Martínez Madrid N. Current status and prospects of automatic sleep stages scoring: Review. Biomed Eng Lett 2023; 13:247-272. [PMID: 37519865 PMCID: PMC10382458 DOI: 10.1007/s13534-023-00299-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/07/2023] [Accepted: 06/18/2023] [Indexed: 08/01/2023] Open
Abstract
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual procedure requires considerable human and financial resources, and incorporates some subjectivity, an automated approach could result in several advantages. There have been many developments in this area, and in order to provide a comprehensive overview, it is essential to review relevant recent works and summarise the characteristics of the approaches, which is the main aim of this article. To achieve it, we examined articles published between 2018 and 2022 that dealt with the automated scoring of sleep stages. In the final selection for in-depth analysis, 125 articles were included after reviewing a total of 515 publications. The results revealed that automatic scoring demonstrates good quality (with Cohen's kappa up to over 0.80 and accuracy up to over 90%) in analysing EEG/EEG + EOG + EMG signals. At the same time, it should be noted that there has been no breakthrough in the quality of results using these signals in recent years. Systems involving other signals that could potentially be acquired more conveniently for the user (e.g. respiratory, cardiac or movement signals) remain more challenging in the implementation with a high level of reliability but have considerable innovation capability. In general, automatic sleep stage scoring has excellent potential to assist medical professionals while providing an objective assessment.
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Affiliation(s)
- Maksym Gaiduk
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
| | | | - Ralf Seepold
- HTWG Konstanz – University of Applied Sciences, Alfred-Wachtel-Str.8, 78462 Konstanz, Germany
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17
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Choi I, Sung W. Single-Channel Ecg-Based Sleep Stage Classification With End-To-End Trainable Deep Neural Networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083334 DOI: 10.1109/embc40787.2023.10340302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The demand for automatic sleep stage classification using easily obtainable signals like electrocardiograms (ECGs) is rising due to the growing number of sleep disorder cases. Our study examined the potential of using single-channel ECG signals for user-friendly automatic sleep stage classification. Unlike previous studies that relied on manual features such as heart rate and variability, we propose using fully neural network-based features. The proposed model uses a ContextNet-based feature encoder applied to the ECG spectrogram, and a Transformer model to capture the temporal properties of sleep cycles over the course of the night.
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18
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van den Broek N, van Meulen F, Ross M, Cerny A, Anderer P, van Gilst M, Pillen S, Overeem S, Fonseca P. Automated sleep staging in people with intellectual disabilities using heart rate and respiration variability. JOURNAL OF INTELLECTUAL DISABILITY RESEARCH : JIDR 2023. [PMID: 37291951 DOI: 10.1111/jir.13060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND People with intellectual disabilities (ID) have a higher risk of sleep disorders. Polysomnography (PSG) remains the diagnostic gold standard in sleep medicine. However, PSG in people with ID can be challenging, as sensors can be burdensome and have a negative influence on sleep. Alternative methods of assessing sleep have been proposed that could potentially transfer to less obtrusive monitoring devices. The goal of this study was to investigate whether analysis of heart rate variability and respiration variability is suitable for the automatic scoring of sleep stages in sleep-disordered people with ID. METHODS Manually scored sleep stages in PSGs of 73 people with ID (borderline to profound) were compared with the scoring of sleep stages by the CardioRespiratory Sleep Staging (CReSS) algorithm. CReSS uses cardiac and/or respiratory input to score the different sleep stages. Performance of the algorithm was analysed using input from electrocardiogram (ECG), respiratory effort and a combination of both. Agreement was determined by means of epoch-per-epoch Cohen's kappa coefficient. The influence of demographics, comorbidities and potential manual scoring difficulties (based on comments in the PSG report) was explored. RESULTS The use of CReSS with combination of both ECG and respiratory effort provided the best agreement in scoring sleep and wake when compared with manually scored PSG (PSG versus ECG = kappa 0.56, PSG versus respiratory effort = kappa 0.53 and PSG versus both = kappa 0.62). Presence of epilepsy or difficulties in manually scoring sleep stages negatively influenced agreement significantly, but nevertheless, performance remained acceptable. In people with ID without epilepsy, the average kappa approximated that of the general population with sleep disorders. CONCLUSIONS Using analysis of heart rate and respiration variability, sleep stages can be estimated in people with ID. This could in the future lead to less obtrusive measurements of sleep using, for example, wearables, more suitable to this population.
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Affiliation(s)
- N van den Broek
- Centre for Sleep Medicine, Kempenhaeghe, Heeze, The Netherlands
| | - F van Meulen
- Centre for Sleep Medicine, Kempenhaeghe, Heeze, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - M Ross
- Sleep and Respiratory Care, Home Healthcare Solutions, Philips Austria GmbH, Vienna, Austria
| | - A Cerny
- Sleep and Respiratory Care, Home Healthcare Solutions, Philips Austria GmbH, Vienna, Austria
| | - P Anderer
- Sleep and Respiratory Care, Home Healthcare Solutions, Philips Austria GmbH, Vienna, Austria
| | - M van Gilst
- Centre for Sleep Medicine, Kempenhaeghe, Heeze, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - S Pillen
- Centre for Sleep Medicine, Kempenhaeghe, Heeze, The Netherlands
| | - S Overeem
- Centre for Sleep Medicine, Kempenhaeghe, Heeze, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - P Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Philips Research, Eindhoven, The Netherlands
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19
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Fonseca P, Ross M, Cerny A, Anderer P, van Meulen F, Janssen H, Pijpers A, Dujardin S, van Hirtum P, van Gilst M, Overeem S. A computationally efficient algorithm for wearable sleep staging in clinical populations. Sci Rep 2023; 13:9182. [PMID: 37280297 DOI: 10.1038/s41598-023-36444-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/03/2023] [Indexed: 06/08/2023] Open
Abstract
This study describes a computationally efficient algorithm for 4-class sleep staging based on cardiac activity and body movements. Using an accelerometer to calculate gross body movements and a reflective photoplethysmographic (PPG) sensor to determine interbeat intervals and a corresponding instantaneous heart rate signal, a neural network was trained to classify between wake, combined N1 and N2, N3 and REM sleep in epochs of 30 s. The classifier was validated on a hold-out set by comparing the output against manually scored sleep stages based on polysomnography (PSG). In addition, the execution time was compared with that of a previously developed heart rate variability (HRV) feature-based sleep staging algorithm. With a median epoch-per-epoch κ of 0.638 and accuracy of 77.8% the algorithm achieved an equivalent performance when compared to the previously developed HRV-based approach, but with a 50-times faster execution time. This shows how a neural network, without leveraging any a priori knowledge of the domain, can automatically "discover" a suitable mapping between cardiac activity and body movements, and sleep stages, even in patients with different sleep pathologies. In addition to the high performance, the reduced complexity of the algorithm makes practical implementation feasible, opening up new avenues in sleep diagnostics.
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Affiliation(s)
- Pedro Fonseca
- Philips Research Eindhoven, High Tech Campus 34, 5656AE, Eindhoven, The Netherlands.
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Marco Ross
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep and Respiratory Care, Philips Austria GmbH, Vienna, Austria
| | - Andreas Cerny
- Sleep and Respiratory Care, Philips Austria GmbH, Vienna, Austria
| | - Peter Anderer
- Sleep and Respiratory Care, Philips Austria GmbH, Vienna, Austria
| | - Fokke van Meulen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Hennie Janssen
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | | | | | | | - Merel van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
| | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, Heeze, The Netherlands
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20
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Li X, Ono C, Warita N, Shoji T, Nakagawa T, Usukura H, Yu Z, Takahashi Y, Ichiji K, Sugita N, Kobayashi N, Kikuchi S, Kimura R, Hamaie Y, Hino M, Kunii Y, Murakami K, Ishikuro M, Obara T, Nakamura T, Nagami F, Takai T, Ogishima S, Sugawara J, Hoshiai T, Saito M, Tamiya G, Fuse N, Fujii S, Nakayama M, Kuriyama S, Yamamoto M, Yaegashi N, Homma N, Tomita H. Comprehensive evaluation of machine learning algorithms for predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability. Front Psychiatry 2023; 14:1104222. [PMID: 37415686 PMCID: PMC10322181 DOI: 10.3389/fpsyt.2023.1104222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/19/2023] [Indexed: 07/08/2023] Open
Abstract
Introduction Perinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV). Methods Nine HRV indicators (features) and sleep-wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep-wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated-shallow sleep, deep sleep, and the two types of wake conditions-was also tested. Results and Discussion In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep-wake conditions. Among the seven features, "the number of interval differences of successive RR intervals greater than 50 ms (NN50)" and "the proportion dividing NN50 by the total number of RR intervals (pNN50)" were useful to predict sleep-wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.
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Affiliation(s)
- Xue Li
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Chiaki Ono
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Noriko Warita
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Tomoka Shoji
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Takashi Nakagawa
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Hitomi Usukura
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Zhiqian Yu
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Yuta Takahashi
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Kei Ichiji
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Norihiro Sugita
- Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai, Japan
| | | | - Saya Kikuchi
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
| | - Ryoko Kimura
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yumiko Hamaie
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Mizuki Hino
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Yasuto Kunii
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Keiko Murakami
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Mami Ishikuro
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Taku Obara
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Tomohiro Nakamura
- Department of Health Record Informatics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Fuji Nagami
- Department of Public Relations and Planning, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Takako Takai
- Department of Health Record Informatics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Soichi Ogishima
- Department of Health Record Informatics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Junichi Sugawara
- Department of Community Medical Supports, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Tetsuro Hoshiai
- Department of Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Masatoshi Saito
- Department of Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Gen Tamiya
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Nobuo Fuse
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Susumu Fujii
- Department of Disaster Medical Informatics, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Masaharu Nakayama
- Department of Disaster Medical Informatics, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Shinichi Kuriyama
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
- Department of Disaster Public Health, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
| | - Masayuki Yamamoto
- Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai, Japan
- Department of Integrative Genomics, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
| | - Nobuo Yaegashi
- Department of Public Relations and Planning, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
- Department of Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Noriyasu Homma
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hiroaki Tomita
- Department of Psychiatry, Tohoku University Graduate School of Medicine, Sendai, Japan
- Department of Psychiatry, Tohoku University Hospital, Sendai, Japan
- Department of Preventive Medicine and Epidemiology, Tohoku University Tohoku Medical Megabank Organization, Sendai, Japan
- Department of Disaster Psychiatry, International Research Institute of Disaster Sciences, Tohoku University, Sendai, Japan
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21
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Song TA, Chowdhury SR, Malekzadeh M, Harrison S, Hoge TB, Redline S, Stone KL, Saxena R, Purcell SM, Dutta J. AI-Driven sleep staging from actigraphy and heart rate. PLoS One 2023; 18:e0285703. [PMID: 37195925 PMCID: PMC10191307 DOI: 10.1371/journal.pone.0285703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 05/02/2023] [Indexed: 05/19/2023] Open
Abstract
Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.
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Affiliation(s)
- Tzu-An Song
- University of Massachusetts Amherst, Amherst, MA, United States of America
| | | | - Masoud Malekzadeh
- University of Massachusetts Amherst, Amherst, MA, United States of America
| | - Stephanie Harrison
- California Pacific Medical Center Research Institute, San Francisco, CA, United States of America
| | - Terri Blackwell Hoge
- California Pacific Medical Center Research Institute, San Francisco, CA, United States of America
| | - Susan Redline
- Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Katie L. Stone
- California Pacific Medical Center Research Institute, San Francisco, CA, United States of America
| | - Richa Saxena
- Massachusetts General Hospital, Boston, MA, United States of America
| | - Shaun M. Purcell
- Brigham and Women’s Hospital, Boston, MA, United States of America
| | - Joyita Dutta
- University of Massachusetts Amherst, Amherst, MA, United States of America
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22
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Trivedi G, Sharma K, Saboo B, Kathirvel S, Konat A, Zapadia V, Prajapati PJ, Benani U, Patel K, Shah S. Humming (Simple Bhramari Pranayama) as a Stress Buster: A Holter-Based Study to Analyze Heart Rate Variability (HRV) Parameters During Bhramari, Physical Activity, Emotional Stress, and Sleep. Cureus 2023; 15:e37527. [PMID: 37193427 PMCID: PMC10182780 DOI: 10.7759/cureus.37527] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/13/2023] [Indexed: 05/18/2023] Open
Abstract
Objective In this study, our goal was to understand the comparative impact of humming, physical activity, emotional stress, and sleep on several heart rate variability (HRV) parameters, including the stress index (SI), and to assess the effectiveness of humming (simple Bhramari) as a stress buster based on the HRV parameters. Methods This pilot study assessed the long-term HRV parameters of 23 participants in terms of four activities: humming (simple Bhramari), physical activity, emotional stress, and sleep. The single-channel Holter device measured the readings, and data was analyzed using Kubios HRV Premium software for time and frequency-domain HRV parameters, including the stress index. Regarding statistical analysis, single-factor ANOVA followed by paired t-test was used to compare the results of HRV parameters "during" the four activities to understand if humming generates the outcome to enhance the autonomic nervous system. Results Our findings revealed that humming generates the lowest stress index compared to all three other activities (physical activity, emotional stress, and sleep). Several additional HRV parameters also supported the positive impact on the autonomic nervous, equivalent to stress reduction. Conclusions Humming (simple Bhramari) can be an effective stress-buster based on the assessment of several HRV parameters during its practice and in comparison with other activities. A regular daily humming routine can help enhance the parasympathetic nervous system and slow down sympathetic activation.
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Affiliation(s)
- Gunjan Trivedi
- Society for Energy & Emotions, Wellness Space, Ahmedabad, IND
| | - Kamal Sharma
- Cardiology, Dr. Kamal Sharma Cardiology Clinic, Ahmedabad, IND
| | - Banshi Saboo
- Department of Endocrinology, Diabetes Care & Hormone Clinic, Ahmedabad, IND
| | - Soundappan Kathirvel
- Community Medicine, Postgraduate Institute of Medical Education and Research, Chandigarh, IND
| | - Ashwati Konat
- Department of Zoology, Biomedical Technology and Human Genetics, Gujarat University, Ahmedabad, IND
| | | | | | - Urva Benani
- Smt. NHL Municipal Medical College, Internal Medicine, Ahmedabad, IND
| | - Kahan Patel
- Internal Medicine, B J Medical College, Ahmedabad, IND
| | - Suchi Shah
- Internal Medicine, AMC MET Medical College, Ahmedabad, IND
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23
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Motin MA, Karmakar C, Palaniswami M, Penzel T, Kumar D. Multi-stage sleep classification using photoplethysmographic sensor. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221517. [PMID: 37063995 PMCID: PMC10090868 DOI: 10.1098/rsos.221517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
The conventional approach to monitoring sleep stages requires placing multiple sensors on patients, which is inconvenient for long-term monitoring and requires expert support. We propose a single-sensor photoplethysmographic (PPG)-based automated multi-stage sleep classification. This experimental study recorded the PPG during the entire night's sleep of 10 patients. Data analysis was performed to obtain 79 features from the recordings, which were then classified according to sleep stages. The classification results using support vector machine (SVM) with the polynomial kernel yielded an overall accuracy of 84.66%, 79.62% and 72.23% for two-, three- and four-stage sleep classification. These results show that it is possible to conduct sleep stage monitoring using only PPG. These findings open the opportunities for PPG-based wearable solutions for home-based automated sleep monitoring.
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Affiliation(s)
- Mohammod Abdul Motin
- Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Kazla, Rajshahi 6204, Bangladesh
| | - Chandan Karmakar
- School of IT, Deakin University, Burwood, Melbourne, VIC 3125, Australia
| | - Marimuthu Palaniswami
- Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Thomas Penzel
- Interdisciplinary Sleep Medicine Center, Charite Universitatsmedizin, 10117 Berlin, Germany
| | - Dinesh Kumar
- School of Electrical and Biomedical Engineering, RMIT University, Melbourne, VIC 3001, Australia
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24
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Attoh-Mensah E, Igor-Gaez I, Vincent L, Bessot N, Nathou C, Etard O. Cardiorespiratory changes associated with micro-arousals during naps. Neurobiol Sleep Circadian Rhythms 2023; 14:100093. [PMID: 36974322 PMCID: PMC10038786 DOI: 10.1016/j.nbscr.2023.100093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 03/15/2023] [Accepted: 03/15/2023] [Indexed: 03/18/2023] Open
Abstract
The autonomic nervous system (ANS) and the central nervous system (CNS) interplay during sleep, particularly during phasic events such as micro-arousals, has been the subject of several studies. The underlying mechanisms of such relationship which remain unclear, specifically during daytime sleep, were partly investigated in this study. Napping polysomnography was performed on two occasions at least one week apart in 15 healthy subjects. The following cardiorespiratory variables were extracted from the recordings: tachogram, pulse transit time (PTT), pulse wave amplitude, respiratory cycle amplitude, and frequency. Two experts first detected micro-arousal events, then, cardiorespiratory variables were averaged by 30-s epochs over 2 min centered on the onset of the micro-arousals. We found that in the 30 s preceding the arousal events as detected on the electroencephalogram (EEG) recordings, there was a decrease in tachogram, pulse wave amplitude, and PTT values while the respiratory amplitude increased. These changes were more prominent in stage N2 and N3 sleep than in stage N1. The present findings provide new insights into the autonomic changes during the pre-arousal period in daytime naps, as all the variables investigated suggest a sympathetic physiological origin for the changes.
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Affiliation(s)
- Elpidio Attoh-Mensah
- Corresponding author. 2 rue des Rochambelles, CS 14032 14 032, Caen, Cedex, France.
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25
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Topalidis P, Heib DPJ, Baron S, Eigl ES, Hinterberger A, Schabus M. The Virtual Sleep Lab-A Novel Method for Accurate Four-Class Sleep Staging Using Heart-Rate Variability from Low-Cost Wearables. SENSORS (BASEL, SWITZERLAND) 2023; 23:2390. [PMID: 36904595 PMCID: PMC10006886 DOI: 10.3390/s23052390] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/07/2023] [Accepted: 02/11/2023] [Indexed: 06/18/2023]
Abstract
Sleep staging based on polysomnography (PSG) performed by human experts is the de facto "gold standard" for the objective measurement of sleep. PSG and manual sleep staging is, however, personnel-intensive and time-consuming and it is thus impractical to monitor a person's sleep architecture over extended periods. Here, we present a novel, low-cost, automatized, deep learning alternative to PSG sleep staging that provides a reliable epoch-by-epoch four-class sleep staging approach (Wake, Light [N1 + N2], Deep, REM) based solely on inter-beat-interval (IBI) data. Having trained a multi-resolution convolutional neural network (MCNN) on the IBIs of 8898 full-night manually sleep-staged recordings, we tested the MCNN on sleep classification using the IBIs of two low-cost (<EUR 100) consumer wearables: an optical heart rate sensor (VS) and a breast belt (H10), both produced by POLAR®. The overall classification accuracy reached levels comparable to expert inter-rater reliability for both devices (VS: 81%, κ = 0.69; H10: 80.3%, κ = 0.69). In addition, we used the H10 and recorded daily ECG data from 49 participants with sleep complaints over the course of a digital CBT-I-based sleep training program implemented in the App NUKKUAA™. As proof of principle, we classified the IBIs extracted from H10 using the MCNN over the course of the training program and captured sleep-related changes. At the end of the program, participants reported significant improvements in subjective sleep quality and sleep onset latency. Similarly, objective sleep onset latency showed a trend toward improvement. Weekly sleep onset latency, wake time during sleep, and total sleep time also correlated significantly with the subjective reports. The combination of state-of-the-art machine learning with suitable wearables allows continuous and accurate monitoring of sleep in naturalistic settings with profound implications for answering basic and clinical research questions.
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Affiliation(s)
- Pavlos Topalidis
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology and Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
| | - Dominik P. J. Heib
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology and Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
- Institut Proschlaf, 5020 Salzburg, Austria
| | - Sebastian Baron
- Department of Mathematics, Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
- Department of Artificial Intelligence and Human Interfaces (AIHI), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
| | - Esther-Sevil Eigl
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology and Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
| | - Alexandra Hinterberger
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology and Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
| | - Manuel Schabus
- Laboratory for Sleep, Cognition and Consciousness Research, Department of Psychology and Centre for Cognitive Neuroscience Salzburg (CCNS), Paris-Lodron University of Salzburg, 5020 Salzburg, Austria
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26
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Sharma M, Makwana P, Chad RS, Acharya UR. A novel automated robust dual-channel EEG-based sleep scoring system using optimal half-band pair linear-phase biorthogonal wavelet filter bank. APPL INTELL 2023; 53:1-19. [PMID: 36777881 PMCID: PMC9906594 DOI: 10.1007/s10489-022-04432-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2022] [Indexed: 02/11/2023]
Abstract
Nowadays, the hectic work life of people has led to sleep deprivation. This may further result in sleep-related disorders and adverse physiological conditions. Therefore, sleep study has become an active research area. Sleep scoring is crucial for detecting sleep-related disorders like sleep apnea, insomnia, narcolepsy, periodic leg movement (PLM), and restless leg syndrome (RLS). Sleep is conventionally monitored in a sleep laboratory using polysomnography (PSG) which is the recording of various physiological signals. The traditional sleep stage scoring (SSG) done by professional sleep scorers is a tedious, strenuous, and time-consuming process as it is manual. Hence, developing a machine-learning model for automatic SSG is essential. In this study, we propose an automated SSG approach based on the biorthogonal wavelet filter bank's (BWFB) novel least squares (LS) design. We have utilized a huge Wisconsin sleep cohort (WSC) database in this study. The proposed study is a pioneering work on automatic sleep stage classification using the WSC database, which includes good sleepers and patients suffering from various sleep-related disorders, including apnea, insomnia, hypertension, diabetes, and asthma. To investigate the generalization of the proposed system, we evaluated the proposed model with the following publicly available databases: cyclic alternating pattern (CAP), sleep EDF, ISRUC, MIT-BIH, and the sleep apnea database from St. Vincent's University. This study uses only two unipolar EEG channels, namely O1-M2 and C3-M2, for the scoring. The Hjorth parameters (HP) are extracted from the wavelet subbands (SBS) that are obtained from the optimal BWFB. To classify sleep stages, the HP features are fed to several supervised machine learning classifiers. 12 different datasets have been created to develop a robust model. A total of 12 classification tasks (CT) have been conducted employing various classification algorithms. Our developed model achieved the best accuracy of 83.2% and Cohen's Kappa of 0.7345 to reliably distinguish five sleep stages, using an ensemble bagged tree classifier with 10-fold cross-validation using WSC data. We also observed that our system is either better or competitive with existing state-of-art systems when we tested with the above-mentioned five databases other than WSC. This method yielded promising results using only two EEG channels using a huge WSC database. Our approach is simple and hence, the developed model can be installed in home-based clinical systems and wearable devices for sleep scoring.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, 380026 India
| | - Paresh Makwana
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, 380026 India
| | - Rajesh Singh Chad
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, 380026 India
| | - U Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354 Taiwan
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore
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27
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Bakker JP, Ross M, Cerny A, Vasko R, Shaw E, Kuna S, Magalang UJ, Punjabi NM, Anderer P. Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring. Sleep 2023; 46:6628222. [PMID: 35780449 PMCID: PMC9905781 DOI: 10.1093/sleep/zsac154] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/22/2022] [Indexed: 11/12/2022] Open
Abstract
STUDY OBJECTIVES To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers. METHODS We applied a new auto-scoring system to three datasets containing 95 PSGs scored by 6-12 scorers, to compare sleep stage probabilities (hypnodensity; i.e. the probability of each sleep stage being assigned to a given epoch) as the primary output, as well as a single sleep stage per epoch assigned by hierarchical majority rule. RESULTS The percentage of epochs with 100% agreement across scorers was 46 ± 9%, 38 ± 10% and 32 ± 9% for the datasets with 6, 9, and 12 scorers, respectively. The mean intra-class correlation coefficient between sleep stage probabilities from auto- and manual-scoring was 0.91, representing excellent reliability. Within each dataset, agreement between auto-scoring and consensus manual-scoring was significantly higher than agreement between manual-scoring and consensus manual-scoring (0.78 vs. 0.69; 0.74 vs. 0.67; and 0.75 vs. 0.67; all p < 0.01). CONCLUSIONS Analysis of scoring performed by multiple scorers reveals that sleep stage ambiguity is the rule rather than the exception. Probabilities of the sleep stages determined by artificial intelligence auto-scoring provide an excellent estimate of this ambiguity. Compared to consensus manual-scoring, sleep staging derived from auto-scoring is for each individual PSG noninferior to manual-scoring meaning that auto-scoring output is ready for interpretation without the need for manual adjustment.
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Affiliation(s)
| | - Marco Ross
- Philips Sleep and Respiratory Care, Vienna, Austria
| | | | - Ray Vasko
- Philips Sleep and Respiratory Care, Pittsburgh, PA,USA
| | - Edmund Shaw
- Philips Sleep and Respiratory Care, Pittsburgh, PA,USA
| | - Samuel Kuna
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,USA.,Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA,USA
| | - Ulysses J Magalang
- Division of Pulmonary, Critical Care, and Sleep Medicine, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Naresh M Punjabi
- Division of Pulmonary, Critical Care, and Sleep Medicine, University of Miami, Miami FL, USA
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28
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van Meulen FB, Grassi A, van den Heuvel L, Overeem S, van Gilst MM, van Dijk JP, Maass H, van Gastel MJH, Fonseca P. Contactless Camera-Based Sleep Staging: The HealthBed Study. BIOENGINEERING (BASEL, SWITZERLAND) 2023; 10:bioengineering10010109. [PMID: 36671681 PMCID: PMC9855193 DOI: 10.3390/bioengineering10010109] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/06/2023] [Accepted: 01/10/2023] [Indexed: 01/15/2023]
Abstract
Polysomnography (PSG) remains the gold standard for sleep monitoring but is obtrusive in nature. Advances in camera sensor technology and data analysis techniques enable contactless monitoring of heart rate variability (HRV). In turn, this may allow remote assessment of sleep stages, as different HRV metrics indirectly reflect the expression of sleep stages. We evaluated a camera-based remote photoplethysmography (PPG) setup to perform automated classification of sleep stages in near darkness. Based on the contactless measurement of pulse rate variability, we use a previously developed HRV-based algorithm for 3 and 4-class sleep stage classification. Performance was evaluated on data of 46 healthy participants obtained from simultaneous overnight recording of PSG and camera-based remote PPG. To validate the results and for benchmarking purposes, the same algorithm was used to classify sleep stages based on the corresponding ECG data. Compared to manually scored PSG, the remote PPG-based algorithm achieved moderate agreement on both 3 class (Wake-N1/N2/N3-REM) and 4 class (Wake-N1/N2-N3-REM) classification, with average κ of 0.58 and 0.49 and accuracy of 81% and 68%, respectively. This is in range with other performance metrics reported on sensing technologies for wearable sleep staging, showing the potential of video-based non-contact sleep staging.
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Affiliation(s)
- Fokke B. van Meulen
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
- Correspondence:
| | - Angela Grassi
- Philips Research, 5656 AE Eindhoven, The Netherlands
| | | | - Sebastiaan Overeem
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
| | - Merel M. van Gilst
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
| | - Johannes P. van Dijk
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Sleep Medicine Center Kempenhaeghe, 5591 VE Heeze, The Netherlands
| | - Henning Maass
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Mark J. H. van Gastel
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Philips Research, 5656 AE Eindhoven, The Netherlands
| | - Pedro Fonseca
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Philips Research, 5656 AE Eindhoven, The Netherlands
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29
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Kim H, Kim D, Oh J. Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy. Front Public Health 2023; 10:1092222. [PMID: 36699913 PMCID: PMC9869419 DOI: 10.3389/fpubh.2022.1092222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023] Open
Abstract
Introduction Sleep is a fundamental and essential physiological process for recovering physiological function. Sleep disturbance or deprivation has been known to be a causative factor of various physiological and psychological disorders. Therefore, sleep evaluation is vital for diagnosing or monitoring those disorders. Although PSG (polysomnography) has been the gold standard for assessing sleep quality and classifying sleep stages, PSG has various limitations for common uses. In substitution for PSG, there has been vigorous research using actigraphy. Methods For classifying sleep stages automatically, we propose machine learning models with HRV (heart rate variability)-related features and acceleration features, which were processed from the actigraphy (Maxim band) data. Those classification results were transformed into a binary classification for estimating sleep efficiency. With 30 subjects, we conducted PSG, and they slept overnight with wrist-type actigraphy. We assessed the performance of four proposed machine learning models. Results With HRV-related and raw features of actigraphy, Cohen's kappa was 0.974 (p < 0.001) for classifying sleep stages into five stages: wake (W), REM (Rapid Eye Movement) (R), Sleep N1 (Non-Rapid Eye Movement Stage 1, S1), Sleep N2 (Non-Rapid Eye Movement Stage 2, S2), Sleep N3 (Non-Rapid Eye Movement Stage 3, S3). In addition, our machine learning model for the estimation of sleep efficiency showed an accuracy of 0.86. Discussion Our model demonstrated that automated sleep classification results could perfectly match the PSG results. Since models with acceleration features showed modest performance in differentiating some sleep stages, further research on acceleration features must be done. In addition, the sleep efficiency model demonstrated modest results. However, an investigation into the effects of HRV-derived and acceleration features is required.
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Affiliation(s)
- Hyejin Kim
- College of Pharmacy, Sookmyung Women's University, Seoul, Republic of Korea
| | | | - Junhyoung Oh
- Center for Information Security Technologies, International Center for Conversing Technology Building, Anam Campus (Science), Korea University, Seoul, Republic of Korea,*Correspondence: Junhyoung Oh ✉
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Ganglberger W, Krishnamurthy PV, Quadri SA, Tesh RA, Bucklin AA, Adra N, Da Silva Cardoso M, Leone MJ, Hemmige A, Rajan S, Panneerselvam E, Paixao L, Higgins J, Ayub MA, Shao YP, Coughlin B, Sun H, Ye EM, Cash SS, Thompson BT, Akeju O, Kuller D, Thomas RJ, Westover MB. Sleep staging in the ICU with heart rate variability and breathing signals. An exploratory cross-sectional study using deep neural networks. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1120390. [PMID: 36926545 PMCID: PMC10013021 DOI: 10.3389/fnetp.2023.1120390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/13/2023] [Indexed: 03/02/2023]
Abstract
Introduction: To measure sleep in the intensive care unit (ICU), full polysomnography is impractical, while activity monitoring and subjective assessments are severely confounded. However, sleep is an intensely networked state, and reflected in numerous signals. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods Methods: We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in age and sex-matched sleep laboratory patients Results: We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p < 0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients Conclusion: The cardiovascular and respiratory networks encode sleep state information, which, together with artificial intelligence methods, can be utilized to measure sleep state in the ICU.
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Affiliation(s)
- Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Sleep and Health Zurich, University of Zurich, Zurich, Switzerland.,Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Parimala Velpula Krishnamurthy
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Syed A Quadri
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Abigail A Bucklin
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Noor Adra
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Madalena Da Silva Cardoso
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Michael J Leone
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Aashritha Hemmige
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Subapriya Rajan
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Ezhil Panneerselvam
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Luis Paixao
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Jasmine Higgins
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Muhammad Abubakar Ayub
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Yu-Ping Shao
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Brian Coughlin
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Elissa M Ye
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - B Taylor Thompson
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Oluwaseun Akeju
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, United States.,Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | | | - Robert J Thomas
- Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Beth Israel Deaconess Medical Center, Department of Medicine, Division of Pulmonary, Critical Care and Sleep, Boston, MA, United States
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, MGH, Boston, MA, United States.,Clinical Data Animation Center (CDAC), Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
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31
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Bin Heyat MB, Akhtar F, Sultana A, Tumrani S, Teelhawod BN, Abbasi R, Amjad Kamal M, Muaad AY, Lai D, Wu K. Role of Oxidative Stress and Inflammation in Insomnia Sleep Disorder and Cardiovascular Diseases: Herbal Antioxidants and Anti-inflammatory Coupled with Insomnia Detection using Machine Learning. Curr Pharm Des 2022; 28:3618-3636. [PMID: 36464881 DOI: 10.2174/1381612829666221201161636] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 09/20/2022] [Accepted: 10/27/2022] [Indexed: 12/07/2022]
Abstract
Insomnia is well-known as trouble in sleeping and enormously influences human life due to the shortage of sleep. Reactive Oxygen Species (ROS) accrue in neurons during the waking state, and sleep has a defensive role against oxidative damage and dissipates ROS in the brain. In contrast, insomnia is the source of inequity between ROS generation and removal by an endogenous antioxidant defense system. The relationship between insomnia, depression, and anxiety disorders damages the cardiovascular systems' immune mechanisms and functions. Traditionally, polysomnography is used in the diagnosis of insomnia. This technique is complex, with a long time overhead. In this work, we have proposed a novel machine learning-based automatic detection system using the R-R intervals extracted from a single-lead electrocardiograph (ECG). Additionally, we aimed to explore the role of oxidative stress and inflammation in sleeping disorders and cardiovascular diseases, antioxidants' effects, and the psychopharmacological effect of herbal medicine. This work has been carried out in steps, which include collecting the ECG signal for normal and insomnia subjects, analyzing the signal, and finally, automatic classification. We used two approaches, including subjects (normal and insomnia), two sleep stages, i.e., wake and rapid eye movement, and three Machine Learning (ML)-based classifiers to complete the classification. A total number of 3000 ECG segments were collected from 18 subjects. Furthermore, using the theranostics approach, the role of mitochondrial dysfunction causing oxidative stress and inflammatory response in insomnia and cardiovascular diseases was explored. The data from various databases on the mechanism of action of different herbal medicines in insomnia and cardiovascular diseases with antioxidant and antidepressant activities were also retrieved. Random Forest (RF) classifier has shown the highest accuracy (subjects: 87.10% and sleep stage: 88.30%) compared to the Decision Tree (DT) and Support Vector Machine (SVM). The results revealed that the suggested method could perform well in classifying the subjects and sleep stages. Additionally, a random forest machine learning-based classifier could be helpful in the clinical discovery of sleep complications, including insomnia. The evidence retrieved from the databases showed that herbal medicine contains numerous phytochemical bioactives and has multimodal cellular mechanisms of action, viz., antioxidant, anti-inflammatory, vasorelaxant, detoxifier, antidepressant, anxiolytic, and cell-rejuvenator properties. Other herbal medicines have a GABA-A receptor agonist effect. Hence, we recommend that the theranostics approach has potential and can be adopted for future research to improve the quality of life of humans.
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Affiliation(s)
- Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Faijan Akhtar
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Arshiya Sultana
- Department of Ilmul Qabalat wa Amraze Niswan, National Institute of Unani Medicine, Ministry of AYUSH, Bengaluru, Karnataka, India
| | - Saifullah Tumrani
- Department of Computer Science, Bahria University, Karachi 75260, Pakistan
| | - Bibi Nushrina Teelhawod
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Rashid Abbasi
- Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment of Ministry of Education, School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China
| | - Mohammad Amjad Kamal
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.,King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia.,Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1207, Bangladesh.,Enzymoics, Novel Global Community Educational Foundation, Hebersham, NSW 2770, Australia
| | - Abdullah Y Muaad
- Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India.,Sana'a Community College, Sana'a 5695, Yemen
| | - Dakun Lai
- BMI-EP Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
| | - Kaishun Wu
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
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Djanian S, Bruun A, Nielsen TD. Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape. Sleep Med 2022; 100:390-403. [PMID: 36206600 DOI: 10.1016/j.sleep.2022.09.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/05/2022] [Indexed: 01/11/2023]
Abstract
Classifying sleep stages in real-time represents considerable potential, for instance in enabling interactive noise masking in noisy environments when persons are in a state of light sleep or to support clinical staff in analyzing sleep patterns etc. However, the current gold standard for classifying sleep stages, Polysomnography (PSG), is too cumbersome to apply outside controlled hospital settings and requires manual as well as highly specialized knowledge to classify sleep stages. Using data from Consumer Sleep Technologies (CSTs) to inform machine learning algorithms represent a promising opportunity for automating the process of classifying sleep stages, also in settings outside the confinements of clinical expert settings. This study reviews 27 papers that use CSTs in combination with Artificial Intelligence (AI) models to classify sleep stages. AI models and their performance are described and compared to synthesize current state of the art in sleep stage classification with CSTs. Furthermore, gaps in the current approaches are shown and how these AI models could be improved in the near-future. Lastly, the challenges of designing interactions for users that are asleep are highlighted pointing towards avenues of more interactive sleep interventions based on AI-infused CSTs solutions.
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Affiliation(s)
- Shagen Djanian
- Aalborg University, Department of Computer Science, Denmark.
| | - Anders Bruun
- Aalborg University, Department of Computer Science, Denmark
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Classification System of National Music Rhythm Spectrogram Based on Biological Neural Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2047576. [PMID: 36275983 PMCID: PMC9581626 DOI: 10.1155/2022/2047576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/15/2022] [Accepted: 09/28/2022] [Indexed: 11/23/2022]
Abstract
National music is a treasure of Chinese traditional culture. It contains the cultural characteristics of various regions and reflects the core value of Chinese traditional culture. Classification technology classifies a large number of unorganized drama documents, which are not labeled, and to some extent, it helps folk music better enter the lives of ordinary people. Simulate folk music of different spectrum and record corresponding music audio under laboratory conditions Through Fourier transform and other methods, music audio is converted into spectrogram, and a total of 2608 two-dimensional spectrogram images are obtained as datasets. The sonogram dataset is imported into the deep convolution neural network GoogLeNet for music type recognition, and the test accuracy is 99.6%. In addition, the parallel GoogLeNet technology based on inverse autoregressive flow is used. The unique improvement is that acoustic features can be quickly converted into corresponding speech time-domain waveforms, reaching the real-time level, improving the efficiency of model training and loading, and outputting speech with higher naturalness. In order to further prove the reliability of the experimental results, the spectrogram datasets are imported into Resnet18 and Shufflenet for training, and the test accuracy of 99.2% is obtained. The results show that this method can effectively classify and recognize music. The experimental results show that this scheme can achieve more accurate classification. The research realizes the recognition of national music through deep learning spectrogram classification for the first time, which is an intelligent and fast new method of classification and recognition.
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Pini N, Ong JL, Yilmaz G, Chee NIYN, Siting Z, Awasthi A, Biju S, Kishan K, Patanaik A, Fifer WP, Lucchini M. An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device. Front Neurosci 2022; 16:974192. [PMID: 36278001 PMCID: PMC9584568 DOI: 10.3389/fnins.2022.974192] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
Background The rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG). Materials and methods This validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch. Results We evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusion The results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.
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Affiliation(s)
- Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
| | - Ju Lynn Ong
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gizem Yilmaz
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Nicholas I. Y. N. Chee
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Zhao Siting
- Electronic and Information Engineering, Imperial College London, London, United Kingdom
| | - Animesh Awasthi
- Department of Biotechnology, Indian Institute of Technology, Kharagpur, India
| | - Siddharth Biju
- Department of Biotechnology, Indian Institute of Technology, Kharagpur, India
| | | | | | - William P. Fifer
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, United States
| | - Maristella Lucchini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, United States
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, NY, United States
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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; 13:1335. [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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36
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Eskandari M, Parvaneh S, Ehsani H, Fain M, Toosizadeh N. Frailty Identification Using Heart Rate Dynamics: A Deep Learning Approach. IEEE J Biomed Health Inform 2022; 26:3409-3417. [PMID: 35196247 PMCID: PMC9342861 DOI: 10.1109/jbhi.2022.3152538] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Previous research showed that frailty can influence autonomic nervous system and consequently heart rate response to physical activities, which can ultimately influence the homeostatic state among older adults. While most studies have focused on resting state heart rate characteristics or heart rate monitoring without controlling for physical activities, the objective of the current study was to classify pre-frail/frail vs non-frail older adults using heart rate response to physical activity (heart rate dynamics). Eighty-eight older adults (≥65 years) were recruited and stratified into frailty groups based on the five-component Fried frailty phenotype. Groups consisted of 27 non-frail (age = 78.80±7.23) and 61 pre-frail/frail (age = 80.63±8.07) individuals. Participants performed a normal speed walking as the physical task, while heart rate was measured using a wearable electrocardiogram recorder. After creating heart rate time series, a long short-term memory model was used to classify participants into frailty groups. In 5-fold cross validation evaluation, the long short-term memory model could classify the two above-mentioned frailty classes with a sensitivity, specificity, F1-score, and accuracy of 83.0%, 80.0%, 87.0%, and 82.0%, respectively. These findings showed that heart rate dynamics classification using long short-term memory without any feature engineering may provide an accurate and objective marker for frailty screening.
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37
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Garcia-Molina G, Jiang J. Interbeat interval-based sleep staging: work in progress toward real-time implementation. Physiol Meas 2022; 43. [PMID: 35297780 DOI: 10.1088/1361-6579/ac5a78] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/03/2022] [Indexed: 01/27/2023]
Abstract
Objective. Cardiac activity changes during sleep enable real-time sleep staging. We developed a deep neural network (DNN) to detect sleep stages using interbeat intervals (IBIs) extracted from electrocardiogram signals.Approach. Data from healthy and apnea subjects were used for training and validation; 2 additional datasets (healthy and sleep disorders subjects) were used for testing. R-peak detection was used to determine IBIs before resampling at 2 Hz; the resulting signal was segmented into 150 s windows (30 s shift). DNN output approximated the probabilities of a window belonging to light, deep, REM, or wake stages. Cohen's Kappa, accuracy, and sensitivity/specificity per stage were determined, and Kappa was optimized using thresholds on probability ratios for each stage versus light sleep.Main results. Mean (SD) Kappa and accuracy for 4 sleep stages were 0.44 (0.09) and 0.65 (0.07), respectively, in healthy subjects. For 3 sleep stages (light+deep, REM, and wake), Kappa and accuracy were 0.52 (0.12) and 0.76 (0.07), respectively. Algorithm performance on data from subjects with REM behavior disorder or periodic limb movement disorder was significantly worse, with Kappa of 0.24 (0.09) and 0.36 (0.12), respectively. Average processing time by an ARM microprocessor for a 300-sample window was 19.2 ms.Significance. IBIs can be obtained from a variety of cardiac signals, including electrocardiogram, photoplethysmography, and ballistocardiography. The DNN algorithm presented is 3 orders of magnitude smaller compared with state-of-the-art algorithms and was developed to perform real-time, IBI-based sleep staging. With high specificity and moderate sensitivity for deep and REM sleep, small footprint, and causal processing, this algorithm may be used across different platforms to perform real-time sleep staging and direct intervention strategies.Novelty & Significance(92/100 words) This article describes the development and testing of a deep neural network-based algorithm to detect sleep stages using interbeat intervals, which can be obtained from a variety of cardiac signals including photoplethysmography, electrocardiogram, and ballistocardiography. Based on the interbeat intervals identified in electrocardiogram signals, the algorithm architecture included a group of convolution layers and a group of long short-term memory layers. With its small footprint, fast processing time, high specificity and good sensitivity for deep and REM sleep, this algorithm may provide a good option for real-time sleep staging to direct interventions.
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Affiliation(s)
| | - Jiewei Jiang
- Sleep Number Labs, San Jose, CA, United States of America
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38
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Yan C, Li P, Yang M, Li Y, Li J, Zhang H, Liu C. Entropy Analysis of Heart Rate Variability in Different Sleep Stages. ENTROPY 2022; 24:e24030379. [PMID: 35327890 PMCID: PMC8947316 DOI: 10.3390/e24030379] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/01/2022] [Accepted: 03/05/2022] [Indexed: 01/02/2023]
Abstract
How the complexity or irregularity of heart rate variability (HRV) changes across different sleep stages and the importance of these features in sleep staging are not fully understood. This study aimed to investigate the complexity or irregularity of the RR interval time series in different sleep stages and explore their values in sleep staging. We performed approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), distribution entropy (DistEn), conditional entropy (CE), and permutation entropy (PermEn) analyses on RR interval time series extracted from epochs that were constructed based on two methods: (1) 270-s epoch length and (2) 300-s epoch length. To test whether adding the entropy measures can improve the accuracy of sleep staging using linear HRV indices, XGBoost was used to examine the abilities to differentiate among: (i) 5 classes [Wake (W), non-rapid-eye-movement (NREM), which can be divide into 3 sub-stages: stage N1, stage N2, and stage N3, and rapid-eye-movement (REM)]; (ii) 4 classes [W, light sleep (combined N1 and N2), deep sleep (N3), and REM]; and (iii) 3 classes: (W, NREM, and REM). SampEn, FuzzyEn, and CE significantly increased from W to N3 and decreased in REM. DistEn increased from W to N1, decreased in N2, and further decreased in N3; it increased in REM. The average accuracy of the three tasks using linear and entropy features were 42.1%, 59.1%, and 60.8%, respectively, based on 270-s epoch length; all were significantly lower than the performance based on 300-s epoch length (i.e., 54.3%, 63.1%, and 67.5%, respectively). Adding entropy measures to the XGBoost model of linear parameters did not significantly improve the classification performance. However, entropy measures, especially PermEn, DistEn, and FuzzyEn, demonstrated greater importance than most of the linear parameters in the XGBoost model.300-s270-s.
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Affiliation(s)
- Chang Yan
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
- Correspondence: (C.Y.); (C.L.)
| | - Peng Li
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA;
| | - Meicheng Yang
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
| | - Yang Li
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
| | - Jianqing Li
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
| | - Hongxing Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China;
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China; (M.Y.); (Y.L.); (J.L.)
- Correspondence: (C.Y.); (C.L.)
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39
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Ioannides AA, Orphanides GA, Liu L. Rhythmicity in heart rate and its surges usher a special period of sleep, a likely home for PGO waves. Curr Res Physiol 2022; 5:118-141. [PMID: 35243361 PMCID: PMC8867048 DOI: 10.1016/j.crphys.2022.02.003] [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: 11/14/2021] [Revised: 02/01/2022] [Accepted: 02/06/2022] [Indexed: 11/30/2022] Open
Abstract
High amplitude electroencephalogram (EEG) events, like unitary K-complex (KC), are used to partition sleep into stages and hence define the hypnogram, a key instrument of sleep medicine. Throughout sleep the heart rate (HR) changes, often as a steady HR increase leading to a peak, what is known as a heart rate surge (HRS). The hypnogram is often unavailable when most needed, when sleep is disturbed and the graphoelements lose their identity. The hypnogram is also difficult to define during normal sleep, particularly at the start of sleep and the periods that precede and follow rapid eye movement (REM) sleep. Here, we use objective quantitative criteria that group together periods that cannot be assigned to a conventional sleep stage into what we call REM0 periods, with the presence of a HRS one of their defining properties. Extended REM0 periods are characterized by highly regular sequences of HRS that generate an infra-low oscillation around 0.05 Hz. During these regular sequence of HRS, and just before each HRS event, we find avalanches of high amplitude events for each one of the mass electrophysiological signals, i.e. related to eye movement, the motor system and the general neural activity. The most prominent features of long REM0 periods are sequences of three to five KCs which we label multiple K-complexes (KCm). Regarding HRS, a clear dissociation is demonstrated between the presence or absence of high gamma band spectral power (55-95 Hz) of the two types of KCm events: KCm events with strong high frequencies (KCmWSHF) cluster just before the peak of HRS, while KCm between HRS show no increase in high gamma band (KCmNOHF). Tomographic estimates of activity from magnetoencephalography (MEG) in pre-KC periods (single and multiple) showed common increases in the cholinergic Nucleus Basalis of Meynert in the alpha band. The direct contrast of KCmWSHF with KCmNOHF showed increases in all subjects in the high sigma band in the base of the pons and in three subjects in both the delta and high gamma bands in the medial Pontine Reticular Formation (mPRF), the putative Long Lead Initial pulse (LLIP) for Ponto-Geniculo-Occipital (PGO) waves.
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Affiliation(s)
- Andreas A. Ioannides
- Lab. for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia, 1065, Cyprus
| | - Gregoris A. Orphanides
- Lab. for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia, 1065, Cyprus
- The English School, Nicosia, 1684, Cyprus
| | - Lichan Liu
- Lab. for Human Brain Dynamics, AAI Scientific Cultural Services Ltd., Nicosia, 1065, Cyprus
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40
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Anderer P, Ross M, Cerny A, Shaw E. Automated Scoring of Sleep and Associated Events. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:107-130. [PMID: 36217081 DOI: 10.1007/978-3-031-06413-5_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Conventionally, sleep and associated events are scored visually by trained technologists according to the rules summarized in the American Academy of Sleep Medicine Manual. Since its first publication in 2007, the manual was continuously updated; the most recent version as of this writing was published in 2020. Human expert scoring is considered as gold standard, even though there is increasing evidence of limited interrater reliability between human scorers. Significant advances in machine learning have resulted in powerful methods for addressing complex classification problems such as automated scoring of sleep and associated events. Evidence is increasing that these autoscoring systems deliver performance comparable to manual scoring and offer several advantages to visual scoring: (1) avoidance of the rather expensive, time-consuming, and difficult visual scoring task that can be performed only by well-trained and experienced human scorers, (2) attainment of consistent scoring results, and (3) proposition of added value such as scoring in real time, sleep stage probabilities per epoch (hypnodensity), estimates of signal quality and sleep/wake-related features, identifications of periods with clinically relevant ambiguities (confidence trends), configurable sensitivity and rule settings, as well as cardiorespiratory sleep staging for home sleep apnea testing. This chapter describes the development of autoscoring systems since the first attempts in the 1970s up to the most recent solutions based on deep neural network approaches which achieve an accuracy that allows to use the autoscoring results directly for review and interpretation by a physician.
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Affiliation(s)
- Peter Anderer
- Philips Sleep and Respiratory Care, Vienna, Austria.
- The Siesta Group Schlafanalyse GmbH, Vienna, Austria.
| | - Marco Ross
- Philips Sleep and Respiratory Care, Vienna, Austria
| | | | - Edmund Shaw
- Philips Sleep and Respiratory Care, Pittsburgh, PA, USA
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41
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Garcia-Molina G. A model characterizing the coupling between slow-wave activity, instantaneous heart rate and heart rate variability during sleep . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:72-75. [PMID: 34891242 DOI: 10.1109/embc46164.2021.9630006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The cyclical and progressively decreasing dynamics of electroencephalogram (EEG) based slow-wave activity (SWA) during sleep reflects the homeostatic component of sleep-wake regulation. The dynamic changes of heart rate (HR) and heart rate variability (HRV) indices during sleep also exhibit quasi-cyclic trends that appear to correlate with SWA. This article proposes a model to characterize the relationship between SWA, HR and HRV in the polar-coordinate (r-θ) domain. Polar coordinates are particularly well-suited to model cyclic shapes with simple (linear) equations in the r-θ plane. Group-level analyses and individual-level ones of the correlations between the polar-coordinate transformations of SWA and HR reveal R2 values of 0.99 and 0.95 respectively. Given that, HR and HRV can be estimated in less obtrusive ways compared to EEG. This research offers relevant options to conveniently monitor sleep SWA.Clinical Relevance- Slow wave activity is a marker of sleep restoration that most prominently manifests in the EEG. This research suggests that an electrocardiography (ECG)-based non-linear model can approximate a polar-coordinate version of SWA. Since ECG correlates can be unobtrusively acquired during sleep, these results suggest that practical SWA monitoring can be achieved through cardiac activity measurements.
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Weber L, Gaiduk M, Seepold R, Madrid NM, Glos M, Penzel T. Enhancing Current Cardiorespiratory-based Approaches of Sleep Stage Classification by Temporal Feature Stacking. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5518-5522. [PMID: 34892374 DOI: 10.1109/embc46164.2021.9630743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen's κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.Clinical relevance- The method can be employed generically to feature sets for (small scale) datasets to improve classification performance for classification problems with temporal relations with random forest classifiers.
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Fedorin I, Slyusarenko K. Consumer Smartwatches As a Portable PSG: LSTM Based Neural Networks for a Sleep-Related Physiological Parameters Estimation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:849-452. [PMID: 34891423 DOI: 10.1109/embc46164.2021.9629597] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Recently, mobile and wearable devices have become an increasingly integral part of our lives. They provide a possibility of detailed health monitoring using noninvasive and user-friendly techniques. However, lack of continuous monitoring, the need of specific sensors, and the limitations in memory and power consumption are only some of the potential drawbacks of such devices. In the current paper a system based on a deep recurrent neural network is developed for an automatic continuous monitoring of sleep-related physiological parameters by means of a wearable biosignal monitoring systems. Smartwatches based algorithm for non-invasive monitoring of sleep stages, respiratory events (including sleep apnea and hypopnea), snore and blood oxygen saturation is developed. Our experimental results demonstrate that proposed model constitutes a noninvasive and inexpensive screening system for sleep-related physiological parameters and pathological states. The model has shown a 77 % accuracy in sleep stages prediction, more than 80 % accuracy in epoch-by-epoch respiratory events classification, above 60 % accuracy in snore events classification and above 70 % accuracy in blood oxygen saturation (SpO2) level classification (for a two class problem with a SpO2 threshold of 95 %).
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Sanchez REA, Wrede JE, Watson RS, de la Iglesia HO, Dervan LA. Actigraphy in mechanically ventilated pediatric ICU patients: comparison to PSG and evaluation of behavioral circadian rhythmicity. Chronobiol Int 2021; 39:117-128. [PMID: 34634983 DOI: 10.1080/07420528.2021.1987451] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Sleep disruption is common in pediatric intensive care unit (PICU) patients, but measuring sleep in this population is challenging. We aimed to evaluate the utility of actigraphy for estimating circadian rhythmicity in mechanically ventilated PICU patients and its accuracy for measuring sleep by comparing it to polysomnogram (PSG). We conducted a single-center prospective observational study of children 6 months - 17 years of age receiving mechanical ventilation and standard, protocolized sedation for acute respiratory failure, excluding children with acute or historical neurologic injury. We enrolled 16 children and monitored them with up to 14 days of actigraphy and 24 hours of simultaneous limited (10 channel) PSG. Daily actigraphy-based activity profiles demonstrated that patients had a high level of nighttime activity (30-41% of total activity), suggesting disrupted circadian activity cycles. Among n = 12 patients with sufficient actigraphy and PSG data overlap, actigraphy-based sleep estimation showed poor agreement with PSG-identified sleep states, with good sensitivity (94%) but poor specificity (28%), low accuracy (70%,) and low agreement (Cohen's kappa = 0.2, 95% CI = 0.08-0.31). Using univariate linear regression, we identified that Cornell Assessment of Pediatric Delirium scores were associated with accuracy of actigraphy but that other clinical factors including sedative medication doses, activity levels, and restraint use were not. In this population, actigraphy did not reliably discern between sleep and wake states. However, in select patients, actigraphy was able to distinguish diurnal variation in activity patterns, and therefore may be useful for evaluating patients' response to circadian-oriented interventions.
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Affiliation(s)
| | - Joanna E Wrede
- Division of Pulmonary and Sleep Medicine, Department of Pediatrics, University of Washington, Seattle, Washington, USA.,Division of Pediatric Neurology, Department of Neurology, University of Washington, Seattle, Washington, USA
| | - R Scott Watson
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, Washington, USA.,Center for Child Health, Behavior, and Development, Seattle Children's Research Institute, Seattle, Washington, USA
| | - Horacio O de la Iglesia
- Department of Biology, University of Washington, Seattle, Washington, USA.,Graduate Program in Neuroscience, University of Washington, Seattle, Washington, USA
| | - Leslie A Dervan
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, Washington, USA.,Center for Clinical and Translational Research, Seattle Children's Research Institute, Seattle, Washington, USA
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45
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Radha M, Fonseca P, Moreau A, Ross M, Cerny A, Anderer P, Long X, Aarts RM. A deep transfer learning approach for wearable sleep stage classification with photoplethysmography. NPJ Digit Med 2021; 4:135. [PMID: 34526643 PMCID: PMC8443610 DOI: 10.1038/s41746-021-00510-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/23/2021] [Indexed: 11/21/2022] Open
Abstract
Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography (PPG) could open the way for better sleep disorder screening and health monitoring. However, PPG is rarely included in large sleep studies with gold-standard sleep annotation from polysomnography. Therefore, training data-intensive state-of-the-art deep neural networks is challenging. In this work a deep recurrent neural network is first trained using a large sleep data set with electrocardiogram (ECG) data (292 participants, 584 recordings) to perform 4-class sleep stage classification (wake, rapid-eye-movement, N1/N2, and N3). A small part of its weights is adapted to a smaller, newer PPG data set (60 healthy participants, 101 recordings) through three variations of transfer learning. Best results (Cohen's kappa of 0.65 ± 0.11, accuracy of 76.36 ± 7.57%) were achieved with the domain and decision combined transfer learning strategy, significantly outperforming the PPG-trained and ECG-trained baselines. This performance for PPG-based 4-class sleep stage classification is unprecedented in literature, bringing home sleep stage monitoring closer to clinical use. The work demonstrates the merit of transfer learning in developing reliable methods for new sensor technologies by reusing similar, older non-wearable data sets. Further study should evaluate our approach in patients with sleep disorders such as insomnia and sleep apnoea.
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Affiliation(s)
- Mustafa Radha
- Philips Research, Eindhoven, the Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Pedro Fonseca
- Philips Research, Eindhoven, the Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | | | | | | | | | - Xi Long
- Philips Research, Eindhoven, the Netherlands.
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
| | - Ronald M Aarts
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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46
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Muller B, Lengellé R. Cross-Gram matrices and their use in transfer learning: Application to automatic REM detection using heart rate. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106280. [PMID: 34333204 DOI: 10.1016/j.cmpb.2021.106280] [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: 08/18/2020] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES while traditional sleep staging is achieved through the visual - expert-based - annotation of a polysomnography, it has the disadvantages of being unpractical and expensive. Alternatives have been developed over the years to relieve sleep staging from its heavy requirements, through the collection of more easily assessable signals and its automation using machine learning. However, these alternatives have their limitations, some due to variabilities among and between subjects, other inherent to their use of sub-discriminative signals. Many new solutions rely on the evaluation of the Autonomic Nervous System (ANS) activation through the assessment of the heart-rate (HR); the latter is modulated by the aforementioned variabilities, which may result in data and concept shifts between what was learned and what we want to classify. Such adversary effects are usually tackled by Transfer Learning, dealing with problems where there are differences between what is known (source) and what we want to classify (target). In this paper, we propose two new kernel-based methods of transfer learning and assess their performances in Rapid-Eye-Movement (REM) sleep stage detection, using solely the heart rate. METHODS our first contribution is the introduction of Kernel-Cross Alignment (KCA), a measure of similarity between a source and a target, which is a direct extension of Kernel-Target Alignment (KTA). To our knowledge, KCA has currently never been studied in the literature. Our second contribution is two alignment-based methods of transfer learning: Kernel-Target Alignment Transfer Learning (KTATL) and Kernel-Cross Alignment Transfer Learning (KCATL). Both methods differ from KTA, whose traditional use is kernel-tuning: in our methods, the kernel has been fixed beforehand, and our objective is the improvement of the estimation of unknown target labels by taking into account how observations relate to each other, which, as it will be explained, allows to transfer knowledge (transfer learning). RESULTS we compare performances with transfer learning (KCATL, KTATL) to performances without transfer using a fixed classifier (a Support Vector Classifier - SVC). In most cases, both transfer learning methods result in an improvement of performances (higher detection rates for a fixed false-alarm rate). Our methods do not require iterative computations. CONCLUSION we observe improved performances using our transfer methods, which are computationally efficient, as they only require the computation of a kernel matrix and are non-iterative. However, some optimisation aspects are still under investigation.
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Affiliation(s)
- Bruno Muller
- Institut Charles Delaunay, UTT, 12 rue Marie Curie, CS 42060, 10004 Troyes CEDEX, France; PPRS, 4E avenue du Général de Gaulle, 68000 Colmar, France.
| | - Régis Lengellé
- Institut Charles Delaunay, UTT, 12 rue Marie Curie, CS 42060, 10004 Troyes CEDEX, France.
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Schutte-Rodin S, Deak M, Khosla S, Goldstein CA, Yurcheshen M, Chiang A, Gault D, Kern J, O'Hearn D, Ryals S, Verma N, Kirsch DB, Baron K, Holfinger S, Miller J, Patel R, Bhargava S, Ramar K. Evaluating consumer and clinical sleep technologies: an American Academy of Sleep Medicine update. J Clin Sleep Med 2021; 17:2275-2282. [PMID: 34314344 DOI: 10.5664/jcsm.9580] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Sharon Schutte-Rodin
- University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | | | - Seema Khosla
- North Dakota Center for Sleep, Fargo, North Dakota
| | | | | | - Ambrose Chiang
- Louis Stokes Cleveland VA Medical Center, Case Western Reserve University, Cleveland, Ohio
| | - Dominic Gault
- Greenville Health System, University of South Carolina, Greenville, South Carolina
| | - Joseph Kern
- New Mexico VA Health Care System, Albuquerque, New Mexico
| | - Daniel O'Hearn
- Department of Medicine, University of Washington, Seattle, Washington
| | - Scott Ryals
- University of Florida Health Sleep Center, Gainesville, Florida
| | | | - Douglas B Kirsch
- Carolinas Healthcare Medical Group Sleep Services, Charlotte, North Carolina
| | - Kelly Baron
- Univeristy of Utah Sleep-Wake Center, Salt Lake City, Utah
| | | | | | - Ruchir Patel
- The Insomnia and Sleep Institute of Arizona, Scottsdale, Arizona
| | - Sumit Bhargava
- Lucille Packard Children's Hospital at Stanford, Palo Alto, California
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48
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Gaiduk M, Perea JJ, Seepold R, Martinez Madrid N, Penzel T, Glos M, Ortega JA. Estimation of Sleep Stages Analyzing Respiratory and Movement Signals. IEEE J Biomed Health Inform 2021; 26:505-514. [PMID: 34310330 DOI: 10.1109/jbhi.2021.3099295] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.
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49
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Bakker JP, Ross M, Vasko R, Cerny A, Fonseca P, Jasko J, Shaw E, White DP, Anderer P. Estimating sleep stages using cardiorespiratory signals: validation of a novel algorithm across a wide range of sleep-disordered breathing severity. J Clin Sleep Med 2021; 17:1343-1354. [PMID: 33660612 DOI: 10.5664/jcsm.9192] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
STUDY OBJECTIVES We have developed the CardioRespiratory Sleep Staging (CReSS) algorithm for estimating sleep stages using heart rate variability and respiration, allowing for estimation of sleep staging during home sleep apnea tests. Our objective was to undertake an epoch-by-epoch validation of algorithm performance against the gold standard of manual polysomnography sleep staging. METHODS Using 296 polysomnographs, we created a limited montage of airflow and heart rate and deployed CReSS to identify each 30-second epoch as wake, light sleep (N1 + N2), deep sleep (N3), or rapid eye movement (REM) sleep. We calculated Cohen's kappa and the percentage of accurately identified epochs. We repeated our analyses after stratification by sleep-disordered breathing (SDB) severity, and after adding thoracic respiratory effort as a backup signal for periods of invalid airflow. RESULTS CReSS discriminated wake/light sleep/deep sleep/REM sleep with 78% accuracy; the kappa value was 0.643 (95% confidence interval, 0.641-0.645). Discrimination of wake/sleep demonstrated a kappa value of 0.711 and accuracy of 89%, non-REM sleep/REM sleep demonstrated a kappa of 0.790 and accuracy of 94%, and light sleep/deep sleep demonstrated a kappa of 0.469 and accuracy of 87%. Kappa values did not vary by more than 0.07 across subgroups of no SDB, mild SDB, moderate SDB, and severe SDB. Accuracy increased to 80%, with a kappa value of 0.680 (95% confidence interval, 0.678-0.682), when CReSS additionally utilized the thoracic respiratory effort signal. CONCLUSIONS We observed substantial agreement between CReSS and the gold-standard comparator of manual sleep staging of polysomnographic signals, which was consistent across the full range of SDB severity. Future research should focus on the extent to which CReSS reduces the discrepancy between the apnea-hypopnea index and the respiratory event index, and the ability of CReSS to identify REM sleep-related obstructive sleep apnea.
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Affiliation(s)
- Jessie P Bakker
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
| | - Marco Ross
- Philips Sleep and Respiratory Care, Vienna, Austria
| | - Ray Vasko
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
| | | | - Pedro Fonseca
- Philips Research, Eindhoven, the Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Jeff Jasko
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
| | - Edmund Shaw
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
| | - David P White
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
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50
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Huysmans D, Borzée P, Buyse B, Testelmans D, Van Huffel S, Varon C. Sleep Diagnostics for Home Monitoring of Sleep Apnea Patients. Front Digit Health 2021; 3:685766. [PMID: 34713155 PMCID: PMC8521961 DOI: 10.3389/fdgth.2021.685766] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/11/2021] [Indexed: 12/04/2022] Open
Abstract
Objectives: Sleep time information is essential for monitoring of obstructive sleep apnea (OSA), as the severity assessment depends on the number of breathing disturbances per hour of sleep. However, clinical procedures for sleep monitoring rely on numerous uncomfortable sensors, which could affect sleeping patterns. Therefore, an automated method to identify sleep intervals from unobtrusive data is required. However, most unobtrusive sensors suffer from data loss and sensitivity to movement artifacts. Thus, current sleep detection methods are inadequate, as these require long intervals of good quality. Moreover, sleep monitoring of OSA patients is often less reliable due to heart rate disturbances, movement and sleep fragmentation. The primary aim was to develop a sleep-wake classifier for sleep time estimation of suspected OSA patients, based on single short-term segments of their cardiac and respiratory signals. The secondary aim was to define metrics to detect OSA patients directly from their predicted sleep-wake pattern and prioritize them for clinical diagnosis. Methods: This study used a dataset of 183 suspected OSA patients, of which 36 test subjects. First, a convolutional neural network was designed for sleep-wake classification based on healthier patients (AHI < 10). It employed single 30 s epochs of electrocardiograms and respiratory inductance plethysmograms. Sleep information and Total Sleep Time (TST) was derived for all patients using the short-term segments. Next, OSA patients were detected based on the average confidence of sleep predictions and the percentage of sleep-wake transitions in the predicted sleep architecture. Results: Sleep-wake classification on healthy, mild and moderate patients resulted in moderate κ scores of 0.51, 0.49, and 0.48, respectively. However, TST estimates decreased in accuracy with increasing AHI. Nevertheless, severe patients were detected with a sensitivity of 78% and specificity of 89%, and prioritized for clinical diagnosis. As such, their inaccurate TST estimate becomes irrelevant. Excluding detected OSA patients resulted in an overall estimated TST with a mean bias error of 21.9 (± 55.7) min and Pearson correlation of 0.74 to the reference. Conclusion: The presented framework offered a realistic tool for unobtrusive sleep monitoring of suspected OSA patients. Moreover, it enabled fast prioritization of severe patients for clinical diagnosis.
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Affiliation(s)
- Dorien Huysmans
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
| | - Pascal Borzée
- Department of Pneumology, UZ Leuven, Leuven, Belgium
| | - Bertien Buyse
- Department of Pneumology, UZ Leuven, Leuven, Belgium
| | | | - Sabine Van Huffel
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
| | - Carolina Varon
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- e-Media Research Lab, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
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