1
|
Vaussenat F, Bhattacharya A, Boudreau P, Boivin DB, Gagnon G, Cloutier SG. Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms. SENSORS (BASEL, SWITZERLAND) 2024; 24:4317. [PMID: 39001096 PMCID: PMC11243930 DOI: 10.3390/s24134317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 06/18/2024] [Accepted: 06/25/2024] [Indexed: 07/16/2024]
Abstract
Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping.
Collapse
Affiliation(s)
- Fabrice Vaussenat
- Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, QC H3C 1K3, Canada; (F.V.); (A.B.); (G.G.)
| | - Abhiroop Bhattacharya
- Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, QC H3C 1K3, Canada; (F.V.); (A.B.); (G.G.)
| | - Philippe Boudreau
- Centre for Study and Treatment of Circadian Rhythms, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada; (P.B.); (D.B.B.)
| | - Diane B. Boivin
- Centre for Study and Treatment of Circadian Rhythms, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada; (P.B.); (D.B.B.)
| | - Ghyslain Gagnon
- Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, QC H3C 1K3, Canada; (F.V.); (A.B.); (G.G.)
| | - Sylvain G. Cloutier
- Department of Electrical Engineering, École de Technologie Supérieure, Université du Québec, Montréal, QC H3C 1K3, Canada; (F.V.); (A.B.); (G.G.)
| |
Collapse
|
2
|
Garbarino S, Bragazzi NL. Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine. J Pers Med 2024; 14:598. [PMID: 38929819 PMCID: PMC11204813 DOI: 10.3390/jpm14060598] [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: 02/23/2024] [Revised: 05/11/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.
Collapse
Affiliation(s)
- Sergio Garbarino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal/Child Sciences (DINOGMI), University of Genoa, 16126 Genoa, Italy;
- Post-Graduate School of Occupational Health, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
- Human Nutrition Unit (HNU), Department of Food and Drugs, University of Parma, 43125 Parma, Italy
| |
Collapse
|
3
|
Jones AM, Itti L, Sheth BR. Expert-level sleep staging using an electrocardiography-only feed-forward neural network. Comput Biol Med 2024; 176:108545. [PMID: 38749325 DOI: 10.1016/j.compbiomed.2024.108545] [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: 11/13/2023] [Revised: 04/05/2024] [Accepted: 04/28/2024] [Indexed: 05/31/2024]
Abstract
Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomnography (PSG). Unfortunately, PSG is an expensive and cumbersome process involving numerous electrodes, often conducted in an unfamiliar clinic and annotated by a professional. Although commercial devices like smartwatches track sleep, their performance is well below PSG. To address these disadvantages, we present a feed-forward neural network that achieves gold-standard levels of agreement using only a single lead of electrocardiography (ECG) data. Specifically, the median five-stage Cohen's kappa is 0.725 on a large, diverse dataset of 5 to 90-year-old subjects. Comparisons with a comprehensive meta-analysis of between-human inter-rater agreement confirm the non-inferior performance of our model. Finally, we developed a novel loss function to align the training objective with Cohen's kappa. Our method offers an inexpensive, automated, and convenient alternative for sleep stage classification-further enhanced by a real-time scoring option. Cardiosomnography, or a sleep study conducted with ECG only, could take expert-level sleep studies outside the confines of clinics and laboratories and into realistic settings. This advancement democratizes access to high-quality sleep studies, considerably enhancing the field of sleep medicine and neuroscience. It makes less-expensive, higher-quality studies accessible to a broader community, enabling improved sleep research and more personalized, accessible sleep-related healthcare interventions.
Collapse
Affiliation(s)
- Adam M Jones
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
| | - Laurent Itti
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Bhavin R Sheth
- Department of Electrical & Computer Engineering, University of Houston, Houston, TX, USA; Center for NeuroEngineering and Cognitive Systems, University of Houston, Houston, TX, USA
| |
Collapse
|
4
|
Yazdi M, Samaee M, Massicotte D. A Review on Automated Sleep Study. Ann Biomed Eng 2024; 52:1463-1491. [PMID: 38493234 DOI: 10.1007/s10439-024-03486-0] [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: 09/07/2023] [Accepted: 02/25/2024] [Indexed: 03/18/2024]
Abstract
In recent years, research on automated sleep analysis has witnessed significant growth, reflecting advancements in understanding sleep patterns and their impact on overall health. This review synthesizes findings from an exhaustive analysis of 87 papers, systematically retrieved from prominent databases such as Google Scholar, PubMed, IEEE Xplore, and ScienceDirect. The selection criteria prioritized studies focusing on methods employed, signal modalities utilized, and machine learning algorithms applied in automated sleep analysis. The overarching goal was to critically evaluate the strengths and weaknesses of the proposed methods, shedding light on the current landscape and future directions in sleep research. An in-depth exploration of the reviewed literature revealed a diverse range of methodologies and machine learning approaches employed in automated sleep studies. Notably, K-Nearest Neighbors (KNN), Ensemble Learning Methods, and Support Vector Machine (SVM) emerged as versatile and potent classifiers, exhibiting high accuracies in various applications. However, challenges such as performance variability and computational demands were observed, necessitating judicious classifier selection based on dataset intricacies. In addition, the integration of traditional feature extraction methods with deep structures and the combination of different deep neural networks were identified as promising strategies to enhance diagnostic accuracy in sleep-related studies. The reviewed literature emphasized the need for adaptive classifiers, cross-modality integration, and collaborative efforts to drive the field toward more accurate, robust, and accessible sleep-related diagnostic solutions. This comprehensive review serves as a solid foundation for researchers and practitioners, providing an organized synthesis of the current state of knowledge in automated sleep analysis. By highlighting the strengths and challenges of various methodologies, this review aims to guide future research toward more effective and nuanced approaches to sleep diagnostics.
Collapse
Affiliation(s)
- Mehran Yazdi
- Laboratory of Signal and System Integration, Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, Canada.
- Signal and Image Processing Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Mahdi Samaee
- Signal and Image Processing Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Daniel Massicotte
- Laboratory of Signal and System Integration, Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, Canada
| |
Collapse
|
5
|
Waqar S, Ghani Khan MU. Sleep stage prediction using multimodal body network and circadian rhythm. PeerJ Comput Sci 2024; 10:e1988. [PMID: 38686009 PMCID: PMC11057653 DOI: 10.7717/peerj-cs.1988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 03/21/2024] [Indexed: 05/02/2024]
Abstract
Quality sleep plays a vital role in living beings as it contributes extensively to the healing process and the removal of waste products from the body. Poor sleep may lead to depression, memory deficits, heart, and metabolic problems, etc. Sleep usually works in cycles and repeats itself by transitioning into different stages of sleep. This study is unique in that it uses wearable devices to collect multiple parameters from subjects and uses this information to predict sleep stages and sleep patterns. For the multivariate multiclass sleep stage prediction problem, we have experimented with both memoryless (ML) and memory-based models on seven database instances, that is, five from the collected dataset and two from the existing datasets. The Random Forest classifier outclassed the ML models that are LR, MLP, kNN, and SVM with accuracy (ACC) of 0.96 and Cohen Kappa 0.96, and the memory-based model long short-term memory (LSTM) performed well on all the datasets with the maximum attained accuracy of 0.88 and Kappa 0.82. The proposed methodology was also validated on a longitudinal dataset, the Multiethnic Study of Atherosclerosis (MESA), with ACC and Kappa of 0.75 and 0.64 for ML models and 0.86 and 0.78 for memory-based models, respectively, and from another benchmarked Apple Watch dataset available on Physio-Net with ACC and Kappa of 0.93 and 0.93 for ML and 0.92 and 0.87 for memory-based models, respectively. The given methodology showed better results than the original work and indicates that the memory-based method works better to capture the sleep pattern.
Collapse
Affiliation(s)
- Sahar Waqar
- Department of Computer Engineering, University of Engineering and Technology, Lahore, Lahore, Punjab, Pakistan
| | - Muhammad Usman Ghani Khan
- Department of Computer Science, University of Engineering and Technology, Lahore, Lahore, Punjab, Pakistan
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
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.
Collapse
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.
| |
Collapse
|
8
|
Birrer V, Elgendi M, Lambercy O, Menon C. Evaluating reliability in wearable devices for sleep staging. NPJ Digit Med 2024; 7:74. [PMID: 38499793 PMCID: PMC10948771 DOI: 10.1038/s41746-024-01016-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 01/18/2024] [Indexed: 03/20/2024] Open
Abstract
Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.
Collapse
Affiliation(s)
- Vera Birrer
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Carlo Menon
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland.
| |
Collapse
|
9
|
van der Woerd C, van Gorp H, Dujardin S, Sastry M, Garcia Caballero H, van Meulen F, van den Elzen S, Overeem S, Fonseca P. Studying sleep: towards the identification of hypnogram features that drive expert interpretation. Sleep 2024; 47:zsad306. [PMID: 38038673 DOI: 10.1093/sleep/zsad306] [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: 07/06/2023] [Revised: 10/18/2023] [Indexed: 12/02/2023] Open
Abstract
STUDY OBJECTIVES Hypnograms contain a wealth of information and play an important role in sleep medicine. However, interpretation of the hypnogram is a difficult task and requires domain knowledge and "clinical intuition." This study aimed to uncover which features of the hypnogram drive interpretation by physicians. In other words, make explicit which features physicians implicitly look for in hypnograms. METHODS Three sleep experts evaluated up to 612 hypnograms, indicating normal or abnormal sleep structure and suspicion of disorders. ElasticNet and convolutional neural network classification models were trained to predict the collected expert evaluations using hypnogram features and stages as input. The models were evaluated using several measures, including accuracy, Cohen's kappa, Matthew's correlation coefficient, and confusion matrices. Finally, model coefficients and visual analytics techniques were used to interpret the models to associate hypnogram features with expert evaluation. RESULTS Agreement between models and experts (Kappa between 0.47 and 0.52) is similar to agreement between experts (Kappa between 0.38 and 0.50). Sleep fragmentation, measured by transitions between sleep stages per hour, and sleep stage distribution were identified as important predictors for expert interpretation. CONCLUSIONS By comparing hypnograms not solely on an epoch-by-epoch basis, but also on these more specific features that are relevant for the evaluation of experts, performance assessment of (automatic) sleep-staging and surrogate sleep trackers may be improved. In particular, sleep fragmentation is a feature that deserves more attention as it is often not included in the PSG report, and existing (wearable) sleep trackers have shown relatively poor performance in this aspect.
Collapse
Affiliation(s)
- Caspar van der Woerd
- Department Mathematics and Computer Science, Eindhoven University of Technology
- Remote Patient Management and Chronic Care, Philips Research, Eindhoven, The Netherlands
| | - Hans van Gorp
- Remote Patient Management and Chronic Care, Philips Research, Eindhoven, The Netherlands
- Department Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | | | | | - Fokke van Meulen
- Department Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep Medicine Center, Kempenhaeghe, Heeze, The Netherlands
| | - Stef van den Elzen
- Department Mathematics and Computer Science, Eindhoven University of Technology
| | - Sebastiaan Overeem
- Department Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Sleep Medicine Center, Kempenhaeghe, Heeze, The Netherlands
| | - Pedro Fonseca
- Remote Patient Management and Chronic Care, Philips Research, Eindhoven, The Netherlands
- Department Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| |
Collapse
|
10
|
Chen J, Zhao D, Chen B, Wang Q, Li Y, Chen J, Bai C, Guo X, Feng X, He X, Zhang L, Yuan J. Correlation of slow-wave sleep with motor and nonmotor progression in Parkinson's disease. Ann Clin Transl Neurol 2024; 11:554-563. [PMID: 38093699 DOI: 10.1002/acn3.51975] [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: 08/04/2023] [Revised: 11/27/2023] [Accepted: 12/03/2023] [Indexed: 03/27/2024] Open
Abstract
OBJECTIVE This study aimed to explore the association between slow-wave sleep and the progression of motor and nonmotor symptoms in patients with PD. METHODS Data were collected from the Parkinson's Progression Markers Initiative study. Slow-wave sleep, also known as deep non-rapid eye movement (DNREM) sleep, was objectively assessed using the Verily Study Watch. Motor function was assessed using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III score, Hoehn and Yahr stage, freezing of gait, motor fluctuations, and dyskinesia severity. Comprehensive assessments were conducted on nonmotor symptoms, including depression, anxiety, global cognitive function, and autonomic dysfunction. Statistical analyses involved repeated-measures analysis of variance and linear regression. RESULTS A total of 102 patients with PD were included in the study, with a median follow-up duration of 3.4 years. In the long DNREM sleep duration group (n = 55), better motor function (DNREM × time interaction: F(1,100) = 4.866, p = 0.030), less severe sexual dysfunction (p = 0.026), and improved activities of daily living (p = 0.033) were observed at the last follow-up visit compared with the short DNREM sleep duration group (n = 47). Reduced DNREM sleep duration is a risk factor for motor progression (β = -0.251, p = 0.021; 95% confidence interval = -0.465 to -0.038). INTERPRETATION The findings suggest an association between longer DNREM sleep duration and slower motor and nonmotor progression in patients with PD.
Collapse
Affiliation(s)
- Jing Chen
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Danhua Zhao
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Baoyu Chen
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Qi Wang
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Yuan Li
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Junyi Chen
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Chaobo Bai
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Xintong Guo
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Xiaotong Feng
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Xiaoyu He
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| | - Lin Zhang
- PF Center of Excellence, Department of Neurology, UC Davis Medical Center, UC Davis School of Medicine, Sacramento, California, USA
| | - Junliang Yuan
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, China
| |
Collapse
|
11
|
Lee MP, Hoang K, Park S, Song YM, Joo EY, Chang W, Kim JH, Kim JK. Imputing missing sleep data from wearables with neural networks in real-world settings. Sleep 2024; 47:zsad266. [PMID: 37819273 DOI: 10.1093/sleep/zsad266] [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: 05/15/2023] [Revised: 09/12/2023] [Indexed: 10/13/2023] Open
Abstract
Sleep is a critical component of health and well-being but collecting and analyzing accurate longitudinal sleep data can be challenging, especially outside of laboratory settings. We propose a simple neural network model titled SOMNI (Sleep data restOration using Machine learning and Non-negative matrix factorIzation [NMF]) for imputing missing rest-activity data from actigraphy, which can enable clinicians to better handle missing data and monitor sleep-wake cycles of individuals with highly irregular sleep-wake patterns. The model consists of two hidden layers and uses NMF to capture hidden longitudinal sleep-wake patterns of individuals with disturbed sleep-wake cycles. Based on this, we develop two approaches: the individual approach imputes missing data based on the data from only one participant, while the global approach imputes missing data based on the data across multiple participants. Our models are tested with shift and non-shift workers' data from three independent hospitals. Both approaches can accurately impute missing data up to 24 hours of long dataset (>50 days) even for shift workers with extremely irregular sleep-wake patterns (AUC > 0.86). On the other hand, for short dataset (~15 days), only the global model is accurate (AUC > 0.77). Our approach can be used to help clinicians monitor sleep-wake cycles of patients with sleep disorders outside of laboratory settings without relying on sleep diaries, ultimately improving sleep health outcomes.
Collapse
Affiliation(s)
- Minki P Lee
- Department of Mathematics, University of Michigan, Ann Arbor, MI, USA
| | - Kien Hoang
- Institute of Mathematics, EPFL, Lausanne, Switzerland
| | - Sungkyu Park
- Department of Artificial Intelligence Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Yun Min Song
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
| | - Eun Yeon Joo
- Department of Neurology, Neuroscience Center, Samsung Biomedical Research Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Won Chang
- Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA
| | - Jee Hyun Kim
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, Republic of Korea
| | - Jae Kyoung Kim
- Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea
- Biomedical Mathematics Group, Institute for Basic Science, Daejeon, Republic of Korea
| |
Collapse
|
12
|
Jeong J, Yoon W, Lee JG, Kim D, Woo Y, Kim DK, Shin HW. Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification. Sleep 2023; 46:zsad242. [PMID: 37703391 DOI: 10.1093/sleep/zsad242] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 08/11/2023] [Indexed: 09/15/2023] Open
Abstract
STUDY OBJECTIVES Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and resolution. In this study, we constructed a standardized image-based PSG dataset in order to overcome the heterogeneity of raw signal data obtained from various PSG devices and various sleep laboratory environments. METHODS All individually exported European data format files containing raw signals were converted into images with an annotation file, which contained the demographics, diagnoses, and sleep statistics. An image-based DL model for automatic sleep staging was developed, compared with a signal-based model, and validated in an external dataset. RESULTS We constructed 10253 image-based PSG datasets using a standardized format. Among these, 7745 diagnostic PSG data were used to develop our DL model. The DL model using the image dataset showed similar performance to the signal-based dataset for the same subject. The overall DL accuracy was greater than 80%, even with severe obstructive sleep apnea. Moreover, for the first time, we showed explainable DL in the field of sleep medicine as visualized key inference regions using Eigen-class activation maps. Furthermore, when a DL model for sleep scoring performs external validation, we achieved a relatively good performance. CONCLUSIONS Our main contribution demonstrates the availability of a standardized image-based dataset, and highlights that changing the data sampling rate or number of sensors may not require retraining, although performance decreases slightly as the number of sensors decreases.
Collapse
Affiliation(s)
- Jaemin Jeong
- Department of Computer Engineering, School of Software, Hallym University, Chuncheon, Republic of Korea
| | | | - Jeong-Gun Lee
- Department of Computer Engineering, School of Software, Hallym University, Chuncheon, Republic of Korea
| | - Dongyoung Kim
- Department of Computer Engineering, School of Software, Hallym University, Chuncheon, Republic of Korea
| | - Yunhee Woo
- Institute of New Frontier Research, Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Dong-Kyu Kim
- OUaR LaB, Inc, Seoul, Republic of Korea
- Institute of New Frontier Research, Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea
- Department of Otorhinolaryngology-Head and Neck Surgery, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon, Republic of Korea¸
| | - Hyun-Woo Shin
- OUaR LaB, Inc, Seoul, Republic of Korea
- Obstructive Upper Airway Research (OUaR) Laboratory, Department of Pharmacology, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
- Sensory Organ Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea
| |
Collapse
|
13
|
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.
Collapse
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.)
| |
Collapse
|
14
|
Garingo M, Katz C, Patel K, Borgloh SMZA, Sabetian P, Durmer J, Chiang S, Rao VR, Stern JM. Four State Sleep Staging From a Multilayered Algorithm Using Electrocardiographic and Actigraphic Data. J Clin Neurophysiol 2023:00004691-990000000-00101. [PMID: 37797263 PMCID: PMC11186678 DOI: 10.1097/wnp.0000000000001038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/07/2023] Open
Abstract
PURPOSE Sleep studies are important to evaluate sleep and sleep-related disorders. The standard test for evaluating sleep is polysomnography, during which several physiological signals are recorded separately and simultaneously with specialized equipment that requires a technologist. Simpler recordings that can model the results of a polysomnography would provide the benefit of expanding the possibilities of sleep recordings. METHODS Using the publicly available sleep data set from the multiethnic study of atherosclerosis and 1769 nights of sleep, we extracted a distinct data subset with engineered features of the biomarkers collected by actigraphic, oxygenation, and electrocardiographic sensors. We then applied scalable models with recurrent neural network and Extreme Gradient Boosting (XGBoost) with a layered approach to produce an algorithm that we then validated with a separate data set of 177 nights. RESULTS The algorithm achieved an overall performance of 0.833 accuracy and 0.736 kappa in classifying into four states: wake, light sleep, deep sleep, and rapid eye movement (REM). Using feature analysis, we demonstrated that heart rate variability is the most salient feature, which is similar to prior reports. CONCLUSIONS Our results demonstrate the potential benefit of a multilayered algorithm and achieved higher accuracy and kappa than previously described approaches for staging sleep. The results further the possibility of simple, wearable devices for sleep staging. Code is available at https://github.com/NovelaNeuro/nEureka-SleepStaging.
Collapse
Affiliation(s)
| | | | - Kramay Patel
- Department of Biomedical Engineering, University of Toronto
| | | | | | | | - Sharon Chiang
- Department of Neurology, University of California, San Francisco
| | - Vikram R. Rao
- Department of Neurology, University of California, San Francisco
| | - John M. Stern
- Department of Neurology, University of California, Los Angeles
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
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.
Collapse
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
| | | |
Collapse
|
17
|
Osanai H, Yamamoto J, Kitamura T. Extracting electromyographic signals from multi-channel LFPs using independent component analysis without direct muscular recording. CELL REPORTS METHODS 2023; 3:100482. [PMID: 37426755 PMCID: PMC10326347 DOI: 10.1016/j.crmeth.2023.100482] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/12/2023] [Accepted: 04/25/2023] [Indexed: 07/11/2023]
Abstract
Electromyography (EMG) has been commonly used for the precise identification of animal behavior. However, it is often not recorded together with in vivo electrophysiology due to the need for additional surgeries and setups and the high risk of mechanical wire disconnection. While independent component analysis (ICA) has been used to reduce noise from field potential data, there has been no attempt to proactively use the removed "noise," of which EMG signals are thought to be one of the major sources. Here, we demonstrate that EMG signals can be reconstructed without direct EMG recording using the "noise" ICA component from local field potentials. The extracted component is highly correlated with directly measured EMG, termed IC-EMG. IC-EMG is useful for measuring an animal's sleep/wake, freezing response, and non-rapid eye movement (NREM)/REM sleep states consistently with actual EMG. Our method has advantages in precise and long-term behavioral measurement in wide-ranging in vivo electrophysiology experiments.
Collapse
Affiliation(s)
- Hisayuki Osanai
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jun Yamamoto
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Takashi Kitamura
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| |
Collapse
|
18
|
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.
Collapse
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
| |
Collapse
|
19
|
Wenjian W, Qian X, Jun X, Zhikun H. DynamicSleepNet: a multi-exit neural network with adaptive inference time for sleep stage classification. Front Physiol 2023; 14:1171467. [PMID: 37250117 PMCID: PMC10213983 DOI: 10.3389/fphys.2023.1171467] [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: 02/22/2023] [Accepted: 04/26/2023] [Indexed: 05/31/2023] Open
Abstract
Sleep is an essential human physiological behavior, and the quality of sleep directly affects a person's physical and mental state. In clinical medicine, sleep stage is an important basis for doctors to diagnose and treat sleep disorders. The traditional method of classifying sleep stages requires sleep experts to classify them manually, and the whole process is time-consuming and laborious. In recent years, with the help of deep learning, automatic sleep stage classification has made great progress, especially networks using multi-modal electrophysiological signals, which have greatly improved in terms of accuracy. However, we found that the existing multimodal networks have a large number of redundant calculations in the process of using multiple electrophysiological signals, and the networks become heavier due to the use of multiple signals, and difficult to be used in small devices. To solve these two problems, this paper proposes DynamicSleepNet, a network that can maximize the use of multiple electrophysiological signals and can dynamically adjust between accuracy and efficiency. DynamicSleepNet consists of three effective feature extraction modules (EFEMs) and three classifier modules, each EFEM is connected to a classifier. Each EFEM is able to extract signal features while making the effective features more prominent and the invalid features are suppressed. The samples processed by the EFEM are given to the corresponding classifier for classification, and if the classifier considers the uncertainty of the sample to be below the threshold we set, the sample can be output early without going through the whole network. We validated our model on four datasets. The results show that the highest accuracy of our model outperforms all baselines. With accuracy close to baselines, our model is faster than the baselines by a factor of several to several tens, and the number of parameters of the model is lower or close. The implementation code is available at: https://github.com/Quinella7291/A-Multi-exit-Neural-Network-with-Adaptive-Inference-Time-for-Sleep-Stage-Classification/.
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Chen Z, Yang Z, Wang D, Zhu X, Ono N, Altaf-Ul-Amin MD, Kanaya S, Huang M. Sleep Staging Framework with Physiologically Harmonized Sub-Networks. Methods 2023; 209:18-28. [PMID: 36436760 DOI: 10.1016/j.ymeth.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 11/15/2022] [Accepted: 11/21/2022] [Indexed: 11/26/2022] Open
Abstract
Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming gold-standard manual scoring procedure. Recently there have seen promising results on automatic stage scoring by extracting spatio-temporal features via deep neural networks from electroencephalogram (EEG). However, such methods fail to consistently yield good performance due to a missing piece in data representation: the medical criterion of the sleep scoring task on top of EEG features. We argue that capturing stage-specific features that satisfy the criterion of sleep medicine is non-trivial for automatic sleep scoring. This paper considers two criteria: Transient stage marker and Overall profile of EEG features, then we propose a physiologically meaningful framework for sleep stage scoring via mixed deep neural networks. The framework consists of two sub-networks: feature extraction networks, constructed in consideration of the physiological characteristics of sleep, and an attention-based scoring decision network. Moreover, we quantize the framework for potential use under an IoT setting. For proof-of-concept, the performance of the proposed framework is demonstrated by introducing multiple sleep datasets with the largest comprising 42,560 h recorded from 5,793 subjects. From the experiment results, the proposed method achieves a competitive stage scoring performance, especially for Wake, N2, and N3, with higher F1 scores of 0.92, 0.86, and 0.88, respectively. Moreover, the feasibility analysis of framework quantization provides a potential for future implementation in the edge computing field and clinical settings.
Collapse
Affiliation(s)
- Zheng Chen
- Graduate School of Engineering Science, Osaka University, Japan.
| | - Ziwei Yang
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Japan
| | - Dong Wang
- Graduate School and Faculty of Information Science and Electrical Engineering, Kyushu University, Japan
| | - Xin Zhu
- Biomedical Information Engineering Lab, The University of Aizu, Japan
| | - Naoaki Ono
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Japan; Data Science Center, Nara Insitute of Science and Technology, Japan
| | - M D Altaf-Ul-Amin
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Japan
| | - Shigehiko Kanaya
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Japan; Data Science Center, Nara Insitute of Science and Technology, Japan
| | - Ming Huang
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Japan; Data Science Center, Nara Insitute of Science and Technology, Japan.
| |
Collapse
|
23
|
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.
Collapse
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
| |
Collapse
|
24
|
Kjaer TW, Rank ML, Hemmsen MC, Kidmose P, Mikkelsen K. Repeated automatic sleep scoring based on ear-EEG is a valuable alternative to manually scored polysomnography. PLOS DIGITAL HEALTH 2022; 1:e0000134. [PMID: 36812563 PMCID: PMC9931275 DOI: 10.1371/journal.pdig.0000134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 09/25/2022] [Indexed: 11/07/2022]
Abstract
While polysomnography (PSG) is the gold standard to quantify sleep, modern technology allows for new alternatives. PSG is obtrusive, affects the sleep it is set out to measure and requires technical assistance for mounting. A number of less obtrusive solutions based on alternative methods have been introduced, but few have been clinically validated. Here we validate one of these solutions, the ear-EEG method, against concurrently recorded PSG in twenty healthy subjects each measured for four nights. Two trained technicians scored the 80 nights of PSG independently, while an automatic algorithm scored the ear-EEG. The sleep stages and eight sleep metrics (Total Sleep Time (TST), Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset, REM latency, REM fraction of TST, N2 fraction of TST, and N3 fraction of TST) were used in the further analysis. We found the sleep metrics: Total Sleep Time, Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset were estimated with high accuracy and precision between automatic sleep scoring and manual sleep scoring. However, the REM latency and REM fraction of sleep showed high accuracy but low precision. Further, the automatic sleep scoring systematically overestimated the N2 fraction of sleep and slightly underestimated the N3 fraction of sleep. We demonstrate that sleep metrics estimated from automatic sleep scoring based on repeated ear-EEG in some cases are more reliably estimated with repeated nights of automatically scored ear-EEG than with a single night of manually scored PSG. Thus, given the obtrusiveness and cost of PSG, ear-EEG seems to be a useful alternative for sleep staging for the single night recording and an advantageous choice for several nights of sleep monitoring.
Collapse
Affiliation(s)
| | | | | | - Preben Kidmose
- Department of Electrical and Computer Engineering, University of Aarhus, Denmark
| | - Kaare Mikkelsen
- Department of Electrical and Computer Engineering, University of Aarhus, Denmark,* E-mail:
| |
Collapse
|
25
|
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.
Collapse
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
| |
Collapse
|
26
|
Chen PW, O’Brien MK, Horin AP, McGee Koch LL, Lee JY, Xu S, Zee PC, Arora VM, Jayaraman A. Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:6190. [PMID: 36015951 PMCID: PMC9414899 DOI: 10.3390/s22166190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/09/2022] [Accepted: 08/13/2022] [Indexed: 06/15/2023]
Abstract
Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data.
Collapse
Affiliation(s)
- Pin-Wei Chen
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA
| | - Megan K. O’Brien
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA
| | - Adam P. Horin
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA
| | - Lori L. McGee Koch
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA
| | | | - Shuai Xu
- Sibel Health Inc., Niles, IL 60714, USA
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA
| | - Phyllis C. Zee
- Center for Circadian and Sleep Medicine, Department of Neurology, Northwestern University, Chicago, IL 60611, USA
| | - Vineet M. Arora
- Department of Medicine, University of Chicago Medicine, Chicago, IL 60637, USA
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA
| |
Collapse
|
27
|
Wang L, Dang S, Chen S, Sun JY, Wang RX, Pan F. Deep-Learning-Based Detection of Paroxysmal Supraventricular Tachycardia Using Sinus-Rhythm Electrocardiograms. J Clin Med 2022; 11:jcm11154578. [PMID: 35956195 PMCID: PMC9369533 DOI: 10.3390/jcm11154578] [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: 06/21/2022] [Revised: 07/18/2022] [Accepted: 07/31/2022] [Indexed: 11/24/2022] Open
Abstract
Background: Paroxysmal supraventricular tachycardia (PSVT) is a common arrhythmia associated with palpitation and a decline in quality of life. However, it is undetectable with sinus-rhythmic ECGs when patients are not in the symptomatic onset stage. Methods: In the current study, a convolution neural network (CNN) was trained with normal-sinus-rhythm standard 12-lead electrocardiographs (ECGs) of negative control patients and PSVT patients to identify patients with unrecognized PSVT. PSVT refers to atrioventricular nodal reentry tachycardia or atrioventricular reentry tachycardia based on a concealed accessory pathway as confirmed by electrophysiological procedure. Negative control group data were obtained from 5107 patients with at least one normal sinus-rhythmic ECG without any palpitation symptoms. All ECGs were randomly allocated to the training, validation and testing datasets in a 7:1:2 ratio. Model performance was evaluated on the testing dataset through F1 score, overall accuracy, area under the curve, sensitivity, specificity and precision. Results: We retrospectively enrolled 407 sinus-rhythm ECGs of PSVT procedural patients and 1794 ECGs of control patients. A total of 2201 ECGs were randomly divided into training (n = 1541), validation (n = 220) and testing (n = 440) datasets. In the testing dataset, the CNN algorithm showed an overall accuracy of 95.5%, sensitivity of 90.2%, specificity of 96.6% and precision of 86.0%. Conclusion: Our study reveals that a well-trained CNN algorithm may be a rapid, effective, inexpensive and reliable method to contribute to the detection of PSVT.
Collapse
Affiliation(s)
- Lei Wang
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
| | - Shipeng Dang
- Department of Cardiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi 214023, China
| | - Shuangxiong Chen
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
| | - Jin-Yu Sun
- Department of Cardiology, The Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China
| | - Ru-Xing Wang
- Department of Cardiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi 214023, China
- Correspondence: (R.-X.W.); (F.P.)
| | - Feng Pan
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
- Correspondence: (R.-X.W.); (F.P.)
| |
Collapse
|
28
|
A comprehensive evaluation of contemporary methods used for automatic sleep staging. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
29
|
Sun C, Hong S, Wang J, Dong X, Han F, Li H. A systematic review of deep learning methods for modeling electrocardiograms during sleep. Physiol Meas 2022; 43. [PMID: 35853448 DOI: 10.1088/1361-6579/ac826e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 07/19/2022] [Indexed: 11/11/2022]
Abstract
Sleep is one of the most important human physiological activities and plays an essential role in human health. Polysomnography (PSG) is the gold standard for measuring sleep quality and disorders, but it is time-consuming, labor-intensive, and prone to errors. Current research has confirmed the correlations between sleep and the respiratory/circulatory system. Electrocardiography (ECG) is convenient to perform, and ECG data are rich in breathing information. Therefore, sleep research based on ECG data has become popular. Currently, deep learning (DL) methods have achieved promising results on predictive health care tasks using ECG signals. Therefore, in this review, we systematically identify recent research studies and analyze them from the perspectives of data, model, and task. We discuss the shortcomings, summarize the findings, and highlight the potential opportunities. For sleep-related tasks, many ECG-based DL methods produce more accurate results than traditional approaches by combining multiple signal features and model structures. Methods that are more interpretable, scalable, and transferable will become ubiquitous in the daily practice of medicine and ambient-assisted-living applications. This paper is the first systematic review of ECG-based DL methods for sleep tasks.
Collapse
Affiliation(s)
- Chenxi Sun
- School of Artificial Intelligence, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing, 100871, CHINA
| | - Shenda Hong
- National Institute of Health Data Science, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing, Beijing, 100871, CHINA
| | - Jingyu Wang
- Sleep Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, CHINA
| | - Xiaosong Dong
- Sleep Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, CHINA
| | - Fang Han
- Sleep Center, Department of Respiratory and Critical Care Medicine, Peking University People's Hospital, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, CHINA
| | - Hongyan Li
- School of Artificial Intelligence, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing, Beijing, 100871, CHINA
| |
Collapse
|
30
|
Deep adaptation network for subject-specific sleep stage classification based on a single-lead ECG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
|
31
|
Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
Collapse
Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| |
Collapse
|
32
|
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.
Collapse
Affiliation(s)
| | - Jiewei Jiang
- Sleep Number Labs, San Jose, CA, United States of America
| |
Collapse
|
33
|
Zhang H, Wang X, Li H, Mehendale S, Guan Y. Auto-annotating sleep stages based on polysomnographic data. PATTERNS 2022; 3:100371. [PMID: 35079710 PMCID: PMC8767308 DOI: 10.1016/j.patter.2021.100371] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/15/2021] [Accepted: 09/28/2021] [Indexed: 11/25/2022]
Abstract
Sleep disorders affect the quality of life, and the clinical diagnosis of sleep disorders is a time-consuming and tedious process requiring recording and annotating polysomnographic records. In this work, we developed an auto-annotation algorithm based on polysomnographic records and a deep learning architecture that predicts sleep stages at the millisecond level. The model improves the efficiency of the polysomnographic record annotation process by automatically annotating each record within 3.8 s of computation time and with high accuracy. Disease-related sleep stages, such as arousal and apnea, can also be identified by this model, which further expands the physiological insights that the model can potentially provide. Finally, we explored the applicability of the model to data collected from a different modality to demonstrate the robustness of the model. Polysomnography enables accurate annotation of sleeping stages by machine learning Apnea/arousal can be more accurately detected by full polysomnography than EEG U-net achieved excellent performance in sequence-to-sequence prediction Our deep learning model achieves human-level accuracy in sleep status annotations
Sleep quality is one of the top public health concerns. Disturbance during sleep will affect peoples' daily executive functions. In addition, some pathological sleeping conditions, such as arousal and apnea, are closely associated with severe health conditions such as cardiovascular diseases. Traditional sleeping surveillance requires laborious human effort while maintaining a limited reproducibility. In this study, we present a fast automatic sleep annotation deep learning model with excellent performances. Our model can annotate sleeping stages as well as sleeping arousal/apnea at the same time, which provides insight for clinical diagnosis of sleeping patients.
Collapse
|
34
|
Investigation of automated sleep staging from cardiorespiratory signals regarding clinical applicability and robustness. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
35
|
Jeong H, Jeong YW, Park Y, Kim K, Park J, Kang DR. Applications of deep learning methods in digital biomarker research using noninvasive sensing data. Digit Health 2022; 8:20552076221136642. [PMID: 36353696 PMCID: PMC9638529 DOI: 10.1177/20552076221136642] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 10/04/2022] [Indexed: 07/02/2024] Open
Abstract
Introduction: Noninvasive digital biomarkers are critical elements in digital healthcare in terms of not only the ease of measurement but also their use of raw data. In recent years, deep learning methods have been put to use to analyze these diverse heterogeneous data; these methods include representation learning for feature extraction and supervised learning for the prediction of these biomarkers. Methods: We introduce clinical cases of digital biomarkers and various deep-learning methods applied according to each data type. In addition, deep learning methods for the integrated analysis of multidimensional heterogeneous data are introduced, and the utility of these data as an integrated digital biomarker is presented. The current status of digital biomarker research is examined by surveying research cases applied to various types of data as well as modeling methods. Results: We present a future research direction for using data from heterogeneous sources together by introducing deep learning methods for dimensionality reduction and mode integration from multimodal digital biomarker studies covering related domains. The integration of multimodality has led to advances in research through the improvement of performance and complementarity between modes. Discussion: The integrative digital biomarker will be more useful for research on diseases that require data from multiple sources to be treated together. Since delicate signals from patients are not missed and the interaction effects between signals are also considered, it will be helpful for immediate detection and more accurate prediction of symptoms.
Collapse
Affiliation(s)
- Hoyeon Jeong
- Department of Biostatistics, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
| | - Yong W Jeong
- Department of Biostatistics, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
| | - Yeonjae Park
- Department of Biostatistics, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
| | - Kise Kim
- School of Health and Environmental Science, Korea University, Seoul, Republic of Korea
| | | | - Dae R Kang
- Department of Biostatistics, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
- Department of Precision Medicine, Yonsei University Wonju College of
Medicine, Wonju, Republic of Korea
| |
Collapse
|
36
|
Zavanelli N, Kim H, Kim J, Herbert R, Mahmood M, Kim YS, Kwon S, Bolus NB, Torstrick FB, Lee CSD, Yeo WH. At-home wireless monitoring of acute hemodynamic disturbances to detect sleep apnea and sleep stages via a soft sternal patch. SCIENCE ADVANCES 2021; 7:eabl4146. [PMID: 34936438 PMCID: PMC8694628 DOI: 10.1126/sciadv.abl4146] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/04/2021] [Indexed: 05/06/2023]
Abstract
Obstructive sleep apnea (OSA) affects more than 900 million adults globally and can create serious health complications when untreated; however, 80% of cases remain undiagnosed. Critically, current diagnostic techniques are fundamentally limited by low throughputs and high failure rates. Here, we report a wireless, fully integrated, soft patch with skin-like mechanics optimized through analytical and computational studies to capture seismocardiograms, electrocardiograms, and photoplethysmograms from the sternum, allowing clinicians to investigate the cardiovascular response to OSA during home sleep tests. In preliminary trials with symptomatic and control subjects, the soft device demonstrated excellent ability to detect blood-oxygen saturation, respiratory effort, respiration rate, heart rate, cardiac pre-ejection period and ejection timing, aortic opening mechanics, heart rate variability, and sleep staging. Last, machine learning is used to autodetect apneas and hypopneas with 100% sensitivity and 95% precision in preliminary at-home trials with symptomatic patients, compared to data scored by professionally certified sleep clinicians.
Collapse
Affiliation(s)
- Nathan Zavanelli
- George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Hojoong Kim
- George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jongsu Kim
- George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Robert Herbert
- George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Musa Mahmood
- George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Yun-Soung Kim
- George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Shinjae Kwon
- George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | | | | | | | - Woon-Hong Yeo
- George W. Woodruff School of Mechanical Engineering, College of Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Center for Human-Centric Interfaces and Engineering at the Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Wallace H. Coulter Department of Biomedical Engineering, Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA
- Institute for Robotics and Intelligent Machines, Neural Engineering Center, Flexible and Wearable Electronics Advanced Research, Institute for Materials, Georgia Institute of Technology, Atlanta, GA 30332, USA
| |
Collapse
|
37
|
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.
Collapse
|
38
|
Ramesh J, Keeran N, Sagahyroon A, Aloul F. Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning. Healthcare (Basel) 2021; 9:healthcare9111450. [PMID: 34828496 PMCID: PMC8622500 DOI: 10.3390/healthcare9111450] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 11/20/2022] Open
Abstract
Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies.
Collapse
|
39
|
EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization. SENSORS 2021; 21:s21206932. [PMID: 34696145 PMCID: PMC8540703 DOI: 10.3390/s21206932] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/15/2021] [Accepted: 10/17/2021] [Indexed: 11/29/2022]
Abstract
Drowsiness is a risk to human lives in many occupations and activities where full awareness is essential for the safe operation of systems and vehicles, such as driving a car or flying an airplane. Although it is one of the main causes of many road accidents, there is still no reliable definition of drowsiness or a system to reliably detect it. Many researchers have observed correlations between frequency-domain features of the EEG signal and drowsiness, such as an increase in the spectral power of the theta band or a decrease in the spectral power of the beta band. In addition, features calculated as ratio indices between these frequency-domain features show further improvements in detecting drowsiness compared to frequency-domain features alone. This work aims to develop novel multichannel ratio indices that take advantage of the diversity of frequency-domain features from different brain regions. In contrast to the state-of-the-art, we use an evolutionary metaheuristic algorithm to find the nearly optimal set of features and channels from which the indices are calculated. Our results show that drowsiness is best described by the powers in delta and alpha bands. Compared to seven existing single-channel ratio indices, our two novel six-channel indices show improvements in (1) statistically significant differences observed between wakefulness and drowsiness segments, (2) precision of drowsiness detection and classification accuracy of the XGBoost algorithm and (3) model performance by saving time and memory during classification. Our work suggests that a more precise definition of drowsiness is needed, and that accurate early detection of drowsiness should be based on multichannel frequency-domain features.
Collapse
|
40
|
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.
Collapse
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
| |
Collapse
|
41
|
Imtiaz SA. A Systematic Review of Sensing Technologies for Wearable Sleep Staging. SENSORS (BASEL, SWITZERLAND) 2021; 21:1562. [PMID: 33668118 PMCID: PMC7956647 DOI: 10.3390/s21051562] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/13/2021] [Accepted: 02/20/2021] [Indexed: 12/15/2022]
Abstract
Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.
Collapse
Affiliation(s)
- Syed Anas Imtiaz
- Wearable Technologies Lab, Imperial College London, London SW7 2AZ, UK
| |
Collapse
|