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de Gans CJ, Burger P, van den Ende ES, Hermanides J, Nanayakkara PWB, Gemke RJBJ, Rutters F, Stenvers DJ. Sleep assessment using EEG-based wearables - A systematic review. Sleep Med Rev 2024; 76:101951. [PMID: 38754209 DOI: 10.1016/j.smrv.2024.101951] [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: 12/29/2023] [Revised: 04/26/2024] [Accepted: 05/06/2024] [Indexed: 05/18/2024]
Abstract
Polysomnography (PSG) is the reference standard of sleep measurement, but is burdensome for the participant and labor intensive. Affordable electroencephalography (EEG)-based wearables are easy to use and are gaining popularity, yet selecting the most suitable device is a challenge for clinicians and researchers. In this systematic review, we aim to provide a comprehensive overview of available EEG-based wearables to measure human sleep. For each wearable, an overview will be provided regarding validated population and reported measurement properties. A systematic search was conducted in the databases OVID MEDLINE, Embase.com and CINAHL. A machine learning algorithm (ASReview) was utilized to screen titles and abstracts for eligibility. In total, 60 papers were selected, covering 34 unique EEG-based wearables. Feasibility studies indicated good tolerance, high compliance, and success rates. The 42 included validation studies were conducted across diverse populations and showed consistently high accuracy in sleep staging detection. Therefore, the recent advancements in EEG-based wearables show great promise as alternative for PSG and for at-home sleep monitoring. Users should consider factors like user-friendliness, comfort, and costs, as these devices vary in features and pricing, impacting their suitability for individual needs.
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Affiliation(s)
- C J de Gans
- Department of Internal Medicine, Section General Internal Medicine Unit Acute Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
| | - P Burger
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Pediatrics, Emma Children's Hospital, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands
| | - E S van den Ende
- Department of Internal Medicine, Section General Internal Medicine Unit Acute Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - J Hermanides
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Anesthesiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - P W B Nanayakkara
- Department of Internal Medicine, Section General Internal Medicine Unit Acute Medicine, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - R J B J Gemke
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Pediatrics, Emma Children's Hospital, Amsterdam University Medical Center, Amsterdam, the Netherlands; Amsterdam Reproduction and Development Research Institute, Amsterdam, the Netherlands
| | - F Rutters
- Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Epidemiology and Data Science, Amsterdam University Medical Center, the Netherlands
| | - D J Stenvers
- Department of Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Department Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism (AGEM), Amsterdam, the Netherlands
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Hammour G, Davies H, Atzori G, Della Monica C, Ravindran KKG, Revell V, Dijk DJ, Mandic DP. From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2024; 12:448-456. [PMID: 38765887 PMCID: PMC11100860 DOI: 10.1109/jtehm.2024.3388852] [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: 11/21/2023] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 05/22/2024]
Abstract
OBJECTIVE Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.
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Affiliation(s)
- Ghena Hammour
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Harry Davies
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Giuseppe Atzori
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Ciro Della Monica
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Kiran K. G. Ravindran
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Victoria Revell
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
| | - Derk-Jan Dijk
- 2Surrey Sleep Research Centre, School of Biosciences, Faculty of Health and Medical SciencesUniversity of SurreyGU2 7XHGuildfordU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
| | - Danilo P. Mandic
- Department of Electrical and Electronic EngineeringImperial College LondonSW7 2BTLondonU.K.
- U.K. Dementia Research Institute, Care Research and Technology CentreSW7 2BTLondonU.K.
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Meiser A, Lena Knoll A, Bleichner MG. High-density ear-EEG for understanding ear-centered EEG. J Neural Eng 2024; 21:016001. [PMID: 38118173 DOI: 10.1088/1741-2552/ad1783] [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: 05/15/2023] [Accepted: 12/20/2023] [Indexed: 12/22/2023]
Abstract
Background. Mobile ear-EEG provides the opportunity to record EEG unobtrusively in everyday life. However, in real-life, the EEG data quickly becomes difficult to interpret, as the neural signal is contaminated by other, non-neural signal contributions. Due to the small number of electrodes in ear-EEG devices, the interpretation of the EEG becomes even more difficult. For meaningful and reliable ear-EEG, it is crucial that the brain signals we wish to record in real life are well-understood and that we make optimal use of the available electrodes. Their placement should be guided by prior knowledge about the characteristics of the signal of interest.Objective.We want to understand the signal we record with ear-EEG and make recommendations on how to optimally place a limited number of electrodes.Approach.We built a high-density ear-EEG with 31 channels spaced densely around one ear. We used it to record four auditory event-related potentials (ERPs): the mismatch negativity, the P300, the N100 and the N400. With this data, we gain an understanding of how different stages of auditory processing are reflected in ear-EEG. We investigate the electrode configurations that carry the most information and use a mass univariate ERP analysis to identify the optimal channel configuration. We additionally use a multivariate approach to investigate the added value of multi-channel recordings.Main results.We find significant condition differences for all ERPs. The different ERPs vary considerably in their spatial extent and different electrode positions are necessary to optimally capture each component. In the multivariate analysis, we find that the investigation of the ERPs benefits strongly from multi-channel ear-EEG.Significance.Our work emphasizes the importance of a strong theoretical and practical background when building and using ear-EEG. We provide recommendations on finding the optimal electrode positions. These results will guide future research employing ear-EEG in real-life scenarios.
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Affiliation(s)
- Arnd Meiser
- Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
- Faculty of Business Studies and Economics, University of Bremen, Bremen, Germany
| | - Anna Lena Knoll
- Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Martin G Bleichner
- Neurophysiology of Everyday Life Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
- Research Center for Neurosensory Science, University of Oldenburg, Oldenburg, Germany
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Mohamed M, Mohamed N, Kim JG. Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review. BIOSENSORS 2023; 13:1019. [PMID: 38131779 PMCID: PMC10741861 DOI: 10.3390/bios13121019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023]
Abstract
Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.
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Affiliation(s)
| | | | - Jae Gwan Kim
- Biomedical Science and Engineering Department, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea; (M.M.); (N.M.)
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Kaongoen N, Choi J, Woo Choi J, Kwon H, Hwang C, Hwang G, Kim BH, Jo S. The future of wearable EEG: a review of ear-EEG technology and its applications. J Neural Eng 2023; 20:051002. [PMID: 37748474 DOI: 10.1088/1741-2552/acfcda] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 09/25/2023] [Indexed: 09/27/2023]
Abstract
Objective.This review paper provides a comprehensive overview of ear-electroencephalogram (EEG) technology, which involves recording EEG signals from electrodes placed in or around the ear, and its applications in the field of neural engineering.Approach.We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field.Main results.Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring.Significance.This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.
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Affiliation(s)
- Netiwit Kaongoen
- Information and Electronics Research Institute, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jaehoon Choi
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jin Woo Choi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, United States of America
| | - Haram Kwon
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Chaeeun Hwang
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Guebin Hwang
- Robotics Program, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Byung Hyung Kim
- Department of Artificial Intelligence, Inha University, Incheon, Republic of Korea
| | - Sungho Jo
- School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
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Petrossian G, Kateb P, Miquet-Westphal F, Cicoira F. Advances in Electrode Materials for Scalp, Forehead, and Ear EEG: A Mini-Review. ACS APPLIED BIO MATERIALS 2023; 6:3019-3032. [PMID: 37493408 DOI: 10.1021/acsabm.3c00322] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Electroencephalogram (EEG) records the electrical activity of neurons in the cerebral cortex and is used extensively to diagnose, treat, and monitor psychiatric and neurological conditions. Reliable contact between the skin and the electrodes is essential for achieving consistency and for obtaining electroencephalographic information. There has been an increasing demand for effective equipment and electrodes to overcome the time-consuming and cumbersome application of traditional systems. Recently, ear-centered EEG has met with growing interest since it can provide good signal quality due to the proximity of the ear to the brain. In addition, it can facilitate mobile and unobtrusive usage due to its smaller size and ease of use, since it can be used without interfering with the patient's daily activities. The purpose of this mini-review is to first introduce the broad range of electrodes used in conventional (scalp) EEG and subsequently discuss the state-of-the-art literature about around- and in-the-ear EEG.
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Affiliation(s)
- Gayaneh Petrossian
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
| | - Pierre Kateb
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
| | | | - Fabio Cicoira
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C 3A7, Canada
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Hammour G, Atzori G, Monica CD, Ravindran KKG, Revell V, Dijk DJ, Mandic DP. Hearables: Automatic Sleep Scoring from Single-Channel Ear-EEG in Older Adults. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083340 DOI: 10.1109/embc40787.2023.10340253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Sleep disorders are a prevalent problem among older adults, yet obtaining an accurate and reliable assessment of sleep quality can be challenging. Traditional polysomnography (PSG) is the gold standard for sleep staging, but is obtrusive, expensive, and requires expert assistance. To this end, we propose a minimally invasive single-channel single ear-EEG automatic sleep staging method for older adults. The method employs features from the frequency, time, and structural complexity domains, which provide a robust classification of sleep stages from a standardised viscoelastic earpiece. Our method is verified on a dataset of older adults and achieves a kappa value of at least 0.61, indicating substantial agreement. This paves the way for a non-invasive, cost-effective, and portable alternative to traditional PSG for sleep staging.
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Chen Y, Zhou E, Wang Y, Wu Y, Xu G, Chen L. The past, present, and future of sleep quality assessment and monitoring. Brain Res 2023; 1810:148333. [PMID: 36931581 DOI: 10.1016/j.brainres.2023.148333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/09/2023] [Accepted: 03/12/2023] [Indexed: 03/17/2023]
Abstract
Sleep quality is considered to be an individual's self-satisfaction with all aspects of the sleep experience. Good sleep not only improves a person's physical, mental and daily functional health, but also improves the quality-of-life level to some extent. In contrast, chronic sleep deprivation can increase the risk of diseases such as cardiovascular diseases, metabolic dysfunction and cognitive and emotional dysfunction, and can even lead to increased mortality. The scientific evaluation and monitoring of sleep quality is an important prerequisite for safeguarding and promoting the physiological health of the body. Therefore, we have compiled and reviewed the existing methods and emerging technologies commonly used for subjective and objective evaluation and monitoring of sleep quality, and found that subjective sleep evaluation is suitable for clinical screening and large-scale studies, while objective evaluation results are more intuitive and scientific, and in the comprehensive evaluation of sleep, if we want to get more scientific monitoring results, we should combine subjective and objective monitoring and dynamic monitoring.
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Affiliation(s)
- Yanyan Chen
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Enyuan Zhou
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Yu Wang
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Yuxiang Wu
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Guodong Xu
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China
| | - Lin Chen
- School of Physical Education, Jianghan University, Wuhan Hubei, 430056, China.
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Choi JY, Jeon S, Kim H, Ha J, Jeon GS, Lee J, Cho SI. Health-Related Indicators Measured Using Earable Devices: Systematic Review. JMIR Mhealth Uhealth 2022; 10:e36696. [PMID: 36239201 PMCID: PMC9709679 DOI: 10.2196/36696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 09/23/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Earable devices are novel, wearable Internet of Things devices that are user-friendly and have potential applications in mobile health care. The position of the ear is advantageous for assessing vital status and detecting diseases through reliable and comfortable sensing devices. OBJECTIVE Our study aimed to review the utility of health-related indicators derived from earable devices and propose an improved definition of disease prevention. We also proposed future directions for research on the health care applications of earable devices. METHODS A systematic review was conducted of the PubMed, Embase, and Web of Science databases. Keywords were used to identify studies on earable devices published between 2015 and 2020. The earable devices were described in terms of target health outcomes, biomarkers, sensor types and positions, and their utility for disease prevention. RESULTS A total of 51 articles met the inclusion criteria and were reviewed, and the frequency of 5 health-related characteristics of earable devices was described. The most frequent target health outcomes were diet-related outcomes (9/51, 18%), brain status (7/51, 14%), and cardiovascular disease (CVD) and central nervous system disease (5/51, 10% each). The most frequent biomarkers were electroencephalography (11/51, 22%), body movements (6/51, 12%), and body temperature (5/51, 10%). As for sensor types and sensor positions, electrical sensors (19/51, 37%) and the ear canal (26/51, 51%) were the most common, respectively. Moreover, the most frequent prevention stages were secondary prevention (35/51, 69%), primary prevention (12/51, 24%), and tertiary prevention (4/51, 8%). Combinations of ≥2 target health outcomes were the most frequent in secondary prevention (8/35, 23%) followed by brain status and CVD (5/35, 14% each) and by central nervous system disease and head injury (4/35, 11% each). CONCLUSIONS Earable devices can provide biomarkers for various health outcomes. Brain status, healthy diet status, and CVDs were the most frequently targeted outcomes among the studies. Earable devices were mostly used for secondary prevention via monitoring of health or disease status. The potential utility of earable devices for primary and tertiary prevention needs to be investigated further. Earable devices connected to smartphones or tablets through cloud servers will guarantee user access to personal health information and facilitate comfortable wearing.
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Affiliation(s)
- Jin-Young Choi
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Seonghee Jeon
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Hana Kim
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Jaeyoung Ha
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Gyeong-Suk Jeon
- Department of Nursing, College of Natural Science, Mokpo National University, Mokpo, Republic of Korea
| | - Jeong Lee
- Department of Nursing, College of Health and Medical Science, Chodang University, Muan, Republic of Korea
| | - Sung-Il Cho
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea
- Institute of Health and Environment, Seoul National University, Seoul, Republic of Korea
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Hsieh JC, Li Y, Wang H, Perz M, Tang Q, Tang KWK, Pyatnitskiy I, Reyes R, Ding H, Wang H. Design of hydrogel-based wearable EEG electrodes for medical applications. J Mater Chem B 2022; 10:7260-7280. [PMID: 35678148 DOI: 10.1039/d2tb00618a] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The electroencephalogram (EEG) is considered to be a promising method for studying brain disorders. Because of its non-invasive nature, subjects take a lower risk compared to some other invasive methods, while the systems record the brain signal. With the technological advancement of neural and material engineering, we are in the process of achieving continuous monitoring of neural activity through wearable EEG. In this article, we first give a brief introduction to EEG bands, circuits, wired/wireless EEG systems, and analysis algorithms. Then, we review the most recent advances in the interfaces used for EEG recordings, focusing on hydrogel-based EEG electrodes. Specifically, the advances for important figures of merit for EEG electrodes are reviewed. Finally, we summarize the potential medical application of wearable EEG systems.
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Affiliation(s)
- Ju-Chun Hsieh
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Yang Li
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C3J7, Canada
| | - Huiqian Wang
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Matt Perz
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Qiong Tang
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kai Wing Kevin Tang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Ilya Pyatnitskiy
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Raymond Reyes
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Hong Ding
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Huiliang Wang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
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Radhakrishnan BL, Kirubakaran E, Jebadurai IJ, Selvakumar AI, Peter JD. Efficacy of Single-Channel EEG: A Propitious Approach for In-home Sleep Monitoring. Front Public Health 2022; 10:839838. [PMID: 35493356 PMCID: PMC9039057 DOI: 10.3389/fpubh.2022.839838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/21/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- B. L. Radhakrishnan
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India
- *Correspondence: B. L. Radhakrishnan
| | - E. Kirubakaran
- Department of Computer Science and Engineering, Grace College of Engineering, HWP Colony, Thoothukudi, India
| | - Immanuel Johnraja Jebadurai
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India
| | - A. Immanuel Selvakumar
- Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - J. Dinesh Peter
- Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, India
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Signal Quality Investigation of a New Wearable Frontal Lobe EEG Device. SENSORS 2022; 22:s22051898. [PMID: 35271044 PMCID: PMC8914983 DOI: 10.3390/s22051898] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 02/24/2022] [Accepted: 02/26/2022] [Indexed: 02/04/2023]
Abstract
The demand for non-laboratory and long-term EEG acquisition in scientific and clinical applications has put forward new requirements for wearable EEG devices. In this paper, a new wearable frontal EEG device called Mindeep was proposed. A signal quality study was then conducted, which included simulated signal tests and signal quality comparison experiments. Simulated signals with different frequencies and amplitudes were used to test the stability of Mindeep’s circuit, and the high correlation coefficients (>0.9) proved that Mindeep has a stable and reliable hardware circuit. The signal quality comparison experiment, between Mindeep and the gold standard device, Neuroscan, included three tasks: (1) resting; (2) auditory oddball; and (3) attention. In the resting state, the average normalized cross-correlation coefficients between EEG signals recorded by the two devices was around 0.72 ± 0.02, Berger effect was observed (p < 0.01), and the comparison results in the time and frequency domain illustrated the ability of Mindeep to record high-quality EEG signals. The significant differences between high tone and low tone in auditory event-related potential collected by Mindeep was observed in N2 and P2. The attention recognition accuracy of Mindeep achieved 71.12% and 74.76% based on EEG features and the XGBoost model in the two attention tasks, respectively, which were higher than that of Neuroscan (70.19% and 72.80%). The results validated the performance of Mindeep as a prefrontal EEG recording device, which has a wide range of potential applications in audiology, cognitive neuroscience, and daily requirements.
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Henao D, Navarrete M, Juez JY, Dinh H, Gómez R, Valderrama M, Le Van Quyen M. Auditory closed‐loop stimulation on sleep slow oscillations using in‐ear EEG sensors. J Sleep Res 2022; 31:e13555. [DOI: 10.1111/jsr.13555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/14/2022] [Accepted: 01/17/2022] [Indexed: 11/30/2022]
Affiliation(s)
- David Henao
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | - Miguel Navarrete
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
- Cardiff University Brain Research Imaging Centre (CUBRIC) School of Psychology Cardiff University Cardiff UK
| | - José Yesith Juez
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | | | - Rodrigo Gómez
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | - Mario Valderrama
- Department of Biomedical Engineering Universidad de Los Andes Bogotá D.C. Colombia
| | - Michel Le Van Quyen
- Laboratoire d’Imagerie Biomédicale (LIB) Inserm U1146/Sorbonne Université UMCR2/UMR7371 CNRS Paris France
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14
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Ne CKH, Muzaffar J, Amlani A, Bance M. Hearables, in-ear sensing devices for bio-signal acquisition: a narrative review. Expert Rev Med Devices 2021; 18:95-128. [PMID: 34904507 DOI: 10.1080/17434440.2021.2014321] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Hearables are ear devices used for multiple purposes including ubiquitous/remote monitoring of vital signals. This can support early detection, prevention, and management of urgent/non-urgent healthcare needs. This review therefore seeks to analyse the challenges and capabilities of hearables used to monitor human physiological signals. AREAS COVERED Studies were identified via search (Medline, Embase, Web of Science, Cochrane Library, Scopus) and conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Bias assessment used the Mixed Methods Appraisal Tool 2018 and Quality Assessment of Diagnostic Accuracy Studies 2nd Edition. 92/631 studies met the inclusion criteria and were qualitatively analysed. The outcomes, applications, advantages and limitations were discussed according to the vital signal measured. The bias risk ranged from low to high, with most studies facing moderate to high risk in subject selection due to small sample sizes. EXPERT OPINION : Most studies reported good outcomes for ear signal acquisition compared to reference devices. To improve practicability and implementation, wireless connectivity, battery life, impact of motion/environmental artifacts and comfort need to be addressed going forward. Hearable technologies have also shown potential synergies with hearing aids. In future, multimodal ear-sensing devices opens the possibility of comprehensive health monitoring within daily life.
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Affiliation(s)
| | - Jameel Muzaffar
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Aakash Amlani
- Department of Ear, Nose and Throat Surgery, Birmingham Children's Hospital, Birmingham, United Kingdom
| | - Manohar Bance
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
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15
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Nielsen JM, Rades D, Kjaer TW. Wearable electroencephalography for ultra-long-term seizure monitoring: a systematic review and future prospects. Expert Rev Med Devices 2021; 18:57-67. [PMID: 34836477 DOI: 10.1080/17434440.2021.2012152] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION : Wearable electroencephalography (EEG) for objective seizure counting might transform the clinical management of epilepsy. Non-EEG modalities have been validated for the detection of convulsive seizures, but there is still an unmet need for the detection of non-convulsive seizures. AREAS COVERED : The main objective of this systematic review was to explore the current status on wearable surface- and subcutaneous EEG for long-term seizure monitoring in epilepsy. We included 17 studies and evaluated the progress on the field, including device specifications, intended populations, and main results on the published studies including diagnostic accuracy measures. Furthermore, we examine the hurdles for widespread clinical implementation. This systematic review and expert opinion both consults the PRISMA guidelines and reflects on the future perspectives of this emerging field. EXPERT OPINION : Wearable EEG for long-term seizure monitoring is an emerging field, with plenty of proposed devices and proof-of-concept clinical validation studies. The possible implications of these devices are immense including objective seizure counting and possibly forecasting. However, the true clinical value of the devices, including effects on patient important outcomes and clinical decision making is yet to be unveiled and large-scale clinical validation trials are called for.
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Affiliation(s)
- Jonas Munch Nielsen
- Department of Neurology, Zealand University Hospital, Region Sjælland. Vestermarksvej 11, 4000 Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
| | - Dirk Rades
- Department of Radiation Oncology, University of Lübeck, Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, Region Sjælland. Vestermarksvej 11, 4000 Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
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16
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Mikkelsen KB, Phan H, Rank ML, Hemmsen MC, de Vos M, Kidmose P. Sleep monitoring using ear-centered setups: Investigating the influence from electrode configurations. IEEE Trans Biomed Eng 2021; 69:1564-1572. [PMID: 34587000 DOI: 10.1109/tbme.2021.3116274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Modern sleep monitoring development is shifting towards the use of unobtrusive sensors combined with algorithms for automatic sleep scoring. Many different combinations of wet and dry electrodes, ear-centered, forehead-mounted or headband-inspired designs have been proposed, alongside an ever growing variety of machine learning algorithms for automatic sleep scoring. OBJECTIVE Among candidate positions, those in the facial area and around the ears have the benefit of being relatively hairless, and in our view deserve extra attention. In this paper, we seek to determine the limits to sleep monitoring quality within this spatial constraint. METHODS We compare 13 different, realistic sensor setups derived from the same data set and analysed with the same pipeline. RESULTS All setups which include both a lateral and an EOG derivation show similar, state-of-the-art performance, with average Cohen's kappa values of at least 0.80. CONCLUSION If large electrode distances are used, positioning is not critical for achieving large sleep-related signal-to-noise-ratio, and hence accurate sleep scoring. SIGNIFICANCE We argue that with the current competitive performance of automated staging approaches, there is a need for establishing an improved benchmark beyond current single human rater scoring.
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17
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Bader CS, Skurla M, Vahia IV. Technology in the Assessment, Treatment, and Management of Depression. Harv Rev Psychiatry 2021; 28:60-66. [PMID: 31913982 DOI: 10.1097/hrp.0000000000000235] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Caroline S Bader
- From Harvard Medical School (Drs. Bader and Vahia) and McLean Hospital (all)
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18
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Phan H, Chen OY, Koch P, Lu Z, McLoughlin I, Mertins A, De Vos M. Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning. IEEE Trans Biomed Eng 2021; 68:1787-1798. [PMID: 32866092 DOI: 10.1109/tbme.2020.3020381] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. METHODS We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain (i.e. the small cohort) to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database. The target domains are purposely adopted to cover different degrees of data mismatch to the source domains. RESULTS Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach. CONCLUSIONS These results suggest the efficacy of the proposed approach in addressing the above-mentioned data-variability and data-inefficiency issues. SIGNIFICANCE As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small.11The source code and the pretrained models are published at https://github.com/pquochuy/sleep_transfer_learning.
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Ahmadzadeh S, Luo J, Wiffen R. Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey. IEEE Rev Biomed Eng 2020; 15:4-22. [PMID: 33104514 DOI: 10.1109/rbme.2020.3033930] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.
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20
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The Sensitivity of Ear-EEG: Evaluating the Source-Sensor Relationship Using Forward Modeling. Brain Topogr 2020; 33:665-676. [PMID: 32833181 PMCID: PMC7593286 DOI: 10.1007/s10548-020-00793-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 08/12/2020] [Indexed: 01/01/2023]
Abstract
Ear-EEG allows to record brain activity in every-day life, for example to study natural behaviour or unhindered social interactions. Compared to conventional scalp-EEG, ear-EEG uses fewer electrodes and covers only a small part of the head. Consequently, ear-EEG will be less sensitive to some cortical sources. Here, we perform realistic electromagnetic simulations to compare cEEGrid ear-EEG with 128-channel cap-EEG. We compute the sensitivity of ear-EEG for different cortical sources, and quantify the expected signal loss of ear-EEG relative to cap-EEG. Our results show that ear-EEG is most sensitive to sources in the temporal cortex. Furthermore, we show how ear-EEG benefits from a multi-channel configuration (i.e. cEEGrid). The pipelines presented here can be adapted to any arrangement of electrodes and can therefore provide an estimate of sensitivity to cortical regions, thereby increasing the chance of successful experiments using ear-EEG.
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21
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Singh G, Tee A, Trakoolwilaiwan T, Taha A, Olivo M. Method of respiratory rate measurement using a unique wearable platform and an adaptive optical-based approach. Intensive Care Med Exp 2020; 8:15. [PMID: 32449051 PMCID: PMC7246231 DOI: 10.1186/s40635-020-00302-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 05/07/2020] [Indexed: 11/29/2022] Open
Abstract
Background An efficient and accurate method of respiratory rate measurement is still missing in hospital general wards and triage. The goal of this study is to propose a method of respiratory rate measurement that has a potential to be used in general wards, triage, and different hospital settings with comparable performance. We propose a method of respiratory rate measurement that combines a unique wearable platform with an adaptive and optical approach. The optical approach is based on a direct-contact optical diffuse reflectance phenomenon. An adaptive algorithm is developed that computes the first respiratory rate and uses it to select a band. The band then chooses a set of unique optimized parameters in the algorithm to calculate and improve the respiratory rate. We developed a study to compare the proposed method against reference manual counts from 82 patients diagnosed with respiratory diseases. Results We found good agreement between the proposed method of respiratory rate measurement and reference manual counts. The performance of the proposed method highlighted deviations with a 95% confidence interval (C.I.) of − 3.34 and 3.67 breaths per minute (bpm) and a mean bias and standard deviation (STD) of 0.05 bpm and 2.56 bpm, respectively. Conclusions The performance of the proposed method of respiratory rate measurement is comparable with current manual counting and other respiratory rate devices reported. The method has additional advantages that include ease-of-use, quick setup time, and being mobile for wider clinical use. The proposed method has the potential as a tool to measure respiratory rates in a number of use cases.
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Affiliation(s)
- Gurpreet Singh
- Laboratory of Bio-Optical Imaging, Singapore Bioimaging Consortium, Agency for Science Technology and Research, Singapore, Singapore.
| | - Augustine Tee
- Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore, Singapore
| | - Thanawin Trakoolwilaiwan
- Laboratory of Bio-Optical Imaging, Singapore Bioimaging Consortium, Agency for Science Technology and Research, Singapore, Singapore
| | - Aza Taha
- Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore, Singapore
| | - Malini Olivo
- Laboratory of Bio-Optical Imaging, Singapore Bioimaging Consortium, Agency for Science Technology and Research, Singapore, Singapore
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22
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Phan H, Chen OY, Koch P, Mertins A, De Vos M. Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1829-1833. [PMID: 31946253 DOI: 10.1109/embc.2019.8857348] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Sleep staging, a process of identifying the sleep stages associated with polysomnography (PSG) epochs, plays an important role in sleep monitoring and diagnosing sleep disorders. We present in this work a model fusion approach to automate this task. The fusion model is composed of two base sleep-stage classifiers, SeqSleepNet and DeepSleepNet, both of which are state-of-the-art end-to-end deep learning models complying to the sequence-to-sequence sleep staging scheme. In addition, in the light of ensemble methods, we reason and demonstrate that these two networks form a good ensemble of models due to their high diversity. Experiments show that the fusion approach is able to preserve the strength of the base networks in the fusion model, leading to consistent performance gains over the two base networks. The fusion model obtain the best modelling results we have observed so far on the Montreal Archive of Sleep Studies (MASS) dataset with 200 subjects, achieving an overall accuracy of 88.0%, a macro F1-score of 84.3%, and a Cohen's kappa of 0.828.
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23
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Guillodo E, Lemey C, Simonnet M, Walter M, Baca-García E, Masetti V, Moga S, Larsen M, Ropars J, Berrouiguet S. Clinical Applications of Mobile Health Wearable-Based Sleep Monitoring: Systematic Review. JMIR Mhealth Uhealth 2020; 8:e10733. [PMID: 32234707 PMCID: PMC7160700 DOI: 10.2196/10733] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 10/04/2019] [Accepted: 10/22/2019] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Sleep disorders are a major public health issue. Nearly 1 in 2 people experience sleep disturbances during their lifetime, with a potential harmful impact on well-being and physical and mental health. OBJECTIVE The aim of this study was to better understand the clinical applications of wearable-based sleep monitoring; therefore, we conducted a review of the literature, including feasibility studies and clinical trials on this topic. METHODS We searched PubMed, PsycINFO, ScienceDirect, the Cochrane Library, Scopus, and the Web of Science through June 2019. We created the list of keywords based on 2 domains: wearables and sleep. The primary selection criterion was the reporting of clinical trials using wearable devices for sleep recording in adults. RESULTS The initial search identified 645 articles; 19 articles meeting the inclusion criteria were included in the final analysis. In all, 4 categories of the selected articles appeared. Of the 19 studies in this review, 58 % (11/19) were comparison studies with the gold standard, 21% (4/19) were feasibility studies, 15% (3/19) were population comparison studies, and 5% (1/19) assessed the impact of sleep disorders in the clinic. The samples were heterogeneous in size, ranging from 1 to 15,839 patients. Our review shows that mobile-health (mHealth) wearable-based sleep monitoring is feasible. However, we identified some major limitations to the reliability of wearable-based monitoring methods compared with polysomnography. CONCLUSIONS This review showed that wearables provide acceptable sleep monitoring but with poor reliability. However, wearable mHealth devices appear to be promising tools for ecological monitoring.
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Affiliation(s)
| | - Christophe Lemey
- IMT Atlantique, Lab-STICC, F-29238 Brest, Brest, France.,EA 7479 SPURRBO, Université de Bretagne Occidentale, Brest, France
| | | | - Michel Walter
- EA 7479 SPURRBO, Université de Bretagne Occidentale, Brest, France
| | | | | | | | - Mark Larsen
- Black Dog Institute, University of New South Wales, Sydney, Australia
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- Please see Acknowledgements for list of collaborators,
| | - Juliette Ropars
- Laboratoire de Traitement de l'Information Médicale, INSERM, UMR 1101, Brest, France.,Department of Child Neurology, University Hospital of Brest, Brest, France
| | - Sofian Berrouiguet
- IMT Atlantique, Lab-STICC, F-29238 Brest, Brest, France.,EA 7479 SPURRBO, Université de Bretagne Occidentale, Brest, France
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24
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Perez-Pozuelo I, Zhai B, Palotti J, Mall R, Aupetit M, Garcia-Gomez JM, Taheri S, Guan Y, Fernandez-Luque L. The future of sleep health: a data-driven revolution in sleep science and medicine. NPJ Digit Med 2020; 3:42. [PMID: 32219183 PMCID: PMC7089984 DOI: 10.1038/s41746-020-0244-4] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/18/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.
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Affiliation(s)
- Ignacio Perez-Pozuelo
- Department of Medicine, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Bing Zhai
- Open Lab, University of Newcastle, Newcastle, UK
| | - Joao Palotti
- Qatar Computing Research Institute, HBKU, Doha, Qatar
- CSAIL, Massachusetts Institute of Technology, Cambridge, MA USA
| | | | | | - Juan M. Garcia-Gomez
- BDSLab, Instituto Universitario de Tecnologias de la Informacion y Comunicaciones-ITACA, Universitat Politecnica de Valencia, Valencia, Spain
| | - Shahrad Taheri
- Department of Medicine and Clinical Research Core, Weill Cornell Medicine - Qatar, Qatar Foundation, Doha, Qatar
| | - Yu Guan
- Open Lab, University of Newcastle, Newcastle, UK
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Jørgensen SD, Zibrandtsen IC, Kjaer TW. Ear-EEG-based sleep scoring in epilepsy: A comparison with scalp-EEG. J Sleep Res 2019; 29:e12921. [PMID: 31621976 DOI: 10.1111/jsr.12921] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 08/21/2019] [Accepted: 08/28/2019] [Indexed: 12/21/2022]
Abstract
Ear-EEG is a wearable electroencephalogram-recording device. It relies on recording electrodes that are nested within a custom-fitted earpiece in the external ear canal. The concept has previously been tested for seizure detection in epileptic patients and for sleep recordings in a healthy population. This study is the first to examine the use of ear-EEG recordings for sleep staging in patients with epilepsy, comparing it with standard recordings from scalp-EEG. We use individuals with epilepsy because of their multiple sleep disturbances, and their complex relationship between seizures and sleep, which make this group very likely to benefit from wearable electroencephalogram devices for sleep if it were introduced in the clinic. The accuracy of the ear-EEG against that of the scalp-EEG is compared for sleep staging, and we evaluate features of sleep architecture in individuals with epilepsy. A mean kappa value of 0.74 is found for the agreement between hypnograms derived from ear-EEG and scalp-EEG. Furthermore, it was discovered that sleep stage transition frequency could be contributing to the kappa variation. These findings are related to other ear-recording systems in the literature, and the potentials and future obstacles of the device are discussed.
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Affiliation(s)
- Sofie D Jørgensen
- Neurological Department, Zealand University Hospital, Roskilde, Denmark
| | | | - Troels W Kjaer
- Neurological Department, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
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26
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Zhao D, Wang Y, Wang Q, Wang X. Comparative analysis of different characteristics of automatic sleep stages. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 175:53-72. [PMID: 31104715 DOI: 10.1016/j.cmpb.2019.04.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 04/03/2019] [Accepted: 04/05/2019] [Indexed: 06/09/2023]
Abstract
BACKGROUND AND OBJECTIVE With the acceleration of social rhythm and the increase of pressure, there are various sleep problems among people. Sleep staging is an important basis for the diagnosis of sleep disorders and other related diseases. The process of automatic staging of sleep is mainly divided into three core steps: data preprocessing, feature extraction, and classification. Accurate analysis of the features of sleep electroencephalogram (EEG) signals is not only helpful to improve the accuracy of sleep staging, but also help people to understand their sleep status. METHODS This paper focuses on the analysis of EEG features during sleep staging, and reviews many feature extraction methods and classification methods for sleep staging and sums up these algorithms used in literatures and its staging results. Besides, this paper lists a total of 22 features based on time domain, time-frequency, and nonlinear analysis methods, including kurtosis, skewness, Hjorth parameters, and standard deviations, wavelets energy; sample entropy (SampEn), fuzzy entropy, Tsallis entropy, fractal dimension (FD), complexity. The data set comes from EDF database. Wavelet transform (WT) and support vector machine (SVM) are used to achieve the sleep staging based on single-channel EEG signal. And the characteristic feature data was analyzed by ANOVA. RESULTS By comparison, the SampEn, fuzzy entropy, FD and complexity can achieve ideal sleep staging. The highest accuracy of sleep staging is 85.93%. The FD and complexity are simpler than the entropy value, but their accuracies are lower. Furthermore, the distribution of these methods in different sleep period is more significant than others, which is content with the results of sleep staging. CONCLUSION In a word, due to the non-stationary and non-linear characteristics of EEG signals, time domain and time-frequency analysis methods all have some limitations. Nonlinear analysis was more effective and practical for the analysis of sleep EEG.
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Affiliation(s)
- Dechun Zhao
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
| | - Yi Wang
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Qiangqiang Wang
- Chongqing Engineering Laboratory of Digital Medical Equipment and Systems, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Xing Wang
- College of Biomedical Engineering, Chongqing University, Chongqing 400044, China
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27
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Nakamura T, Alqurashi YD, Morrell MJ, Mandic DP. Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor. IEEE Trans Biomed Eng 2019; 67:203-212. [PMID: 31021747 DOI: 10.1109/tbme.2019.2911423] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Advances in sensor miniaturization and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. METHODS A total of 22 healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography recordings. The ear-EEG data were analyzed in the both structural complexity and spectral domains. The extracted features were used for automatic sleep stage prediction through supervized machine learning, whereby the PSG data were manually scored by a sleep clinician. RESULTS The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1% in the accuracy over five sleep stage classification. This is supported by a substantial agreement in the kappa metric (0.61). CONCLUSION The in-ear sensor is feasible for monitoring overnight sleep outside the sleep laboratory and also mitigates technical difficulties associated with PSG. It, therefore, represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. SIGNIFICANCE The "standardized" one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.
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Mikkelsen KB, Ebajemito JK, Bonmati‐Carrion MA, Santhi N, Revell VL, Atzori G, della Monica C, Debener S, Dijk D, Sterr A, de Vos M. Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy. J Sleep Res 2019; 28:e12786. [PMID: 30421469 PMCID: PMC6446944 DOI: 10.1111/jsr.12786] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 09/23/2018] [Accepted: 10/05/2018] [Indexed: 12/22/2022]
Abstract
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a low-cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex-printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self-applicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier ("random forests") and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter-individual variation in sleep parameters. The results demonstrate that machine-learning-based scoring of around-the-ear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machine-learning-based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine-learning-based scoring holds promise for large-scale sleep studies.
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Affiliation(s)
- Kaare B. Mikkelsen
- Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
- Department of EngineeringAarhus UniversityAarhusDenmark
| | | | | | | | | | | | | | - Stefan Debener
- Cluster of Excellence Hearing4AllOldenburgGermany
- Department of PsychologyUniversity of OldenburgOldenburgGermany
| | - Derk‐Jan Dijk
- Surrey Sleep Research CentreUniversity of SurreySurreyUK
- Surrey Clinical Research CentreUniversity of SurreySurreyUK
| | | | - Maarten de Vos
- Institute of Biomedical EngineeringUniversity of OxfordOxfordUK
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Phan H. SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging. IEEE Trans Neural Syst Rehabil Eng 2019; 27:400-410. [PMID: 30716040 PMCID: PMC6481557 DOI: 10.1109/tnsre.2019.2896659] [Citation(s) in RCA: 168] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http://github.com/pquochuy/SeqSleepNet). At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modeling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modeling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms the state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.
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Affiliation(s)
- Huy Phan
- School of Computing, University of Kent, Chatham Maritime, Kent ME4 4AG, United Kingdom and the Institute of Biomedical Engineering, University of Oxford, Oxford OX3 7DQ, United Kingdom
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Fallahzadeh R, Rokni SA, Ghasemzadeh H, Soto-Perez-de-Celis E, Shahrokni A. Digital Health for Geriatric Oncology. JCO Clin Cancer Inform 2018; 2:1-12. [DOI: 10.1200/cci.17.00133] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
In this review, we describe state-of-the-art digital health solutions for geriatric oncology and explore the potential application of emerging remote health-monitoring technologies in the context of cancer care. We also discuss the benefits and motivations behind adopting technology for symptom monitoring of older adults with cancer. We provide an overview of common symptoms and of the digital solutions–designed remote symptom assessment. We describe state-of-the-art systems for this purpose and highlight the limitations and challenges for the full-scale adoption of such solutions in geriatric oncology. With rapid advances in Internet-of-things technologies, many remote assessment systems have been developed in recent years. Despite showing potential in several health care domains and reliable functionality, few of these solutions have been designed for or tested in older patients with cancer. As a result, the geriatric oncology community lacks a consensus understanding of a possible correlation between remote digital assessments and health-related outcomes. Although the recent development of digital health solutions has been shown to be reliable and effective in many health-related applications, there exists an unmet need for development of systems and clinical trials specifically designed for remote cancer management of older adults with cancer, including developing advanced remote technologies for cancer-related symptom assessment and psychological behavior monitoring at home and developing outcome-oriented study protocols for accurate evaluation of existing or emerging systems. We conclude that perhaps the clearest path to future large-scale use of remote digital health technologies in cancer research is designing and conducting collaborative studies involving computer scientists, oncologists, and patient advocates.
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Affiliation(s)
- Ramin Fallahzadeh
- Ramin Fallahzadeh, Seyed Ali Rokni, and Hassan Ghasemzadeh, Washington State University, Pullman, WA; Enrique Soto-Perez-de-Celis, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; and Armin Shahrokni, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Seyed Ali Rokni
- Ramin Fallahzadeh, Seyed Ali Rokni, and Hassan Ghasemzadeh, Washington State University, Pullman, WA; Enrique Soto-Perez-de-Celis, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; and Armin Shahrokni, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Hassan Ghasemzadeh
- Ramin Fallahzadeh, Seyed Ali Rokni, and Hassan Ghasemzadeh, Washington State University, Pullman, WA; Enrique Soto-Perez-de-Celis, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; and Armin Shahrokni, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Enrique Soto-Perez-de-Celis
- Ramin Fallahzadeh, Seyed Ali Rokni, and Hassan Ghasemzadeh, Washington State University, Pullman, WA; Enrique Soto-Perez-de-Celis, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; and Armin Shahrokni, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Armin Shahrokni
- Ramin Fallahzadeh, Seyed Ali Rokni, and Hassan Ghasemzadeh, Washington State University, Pullman, WA; Enrique Soto-Perez-de-Celis, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; and Armin Shahrokni, Memorial Sloan Kettering Cancer Center, New York, NY
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Multi-Class Sleep Stage Analysis and Adaptive
Pattern Recognition. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8050697] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Abstract
PURPOSE OF REVIEW To provide guidance in the management of mild obstructive sleep apnoea syndrome (OSAS) in the context of a very high prevalence, poor correlation with symptom profile, and lack of evidence that mild OSAS significantly contributes to comorbidity or early mortality. RECENT FINDINGS Mild obstructive sleep apnoea defined by hourly frequency of apnoeas or hypopnoeas (AHI) between 5 and 15 affects up to 35% of the general adult population but is much less prevalent when associated daytime symptoms are included. The poor correlation between symptoms and AHI complicates diagnosis and reports that mild OSAS is not significantly associated with comorbidity casts doubt on clinical significance. The diagnosis is complicated by night-to-night variability and by underestimation of AHI in ambulatory sleep studies that do not include sleep assessment. Active management of mild OSAS can be symptom-driven and offers a broad range of options. Lifestyle measures may be sufficient in many cases and mandibular advancement devices or positional therapy may be more effective in mild OSAS. Sleepy patients with low AHI may warrant a trial of continuous positive airway pressure therapy to establish the relationship between sleep disordered breathing and symptoms. SUMMARY Management of mild OSAS can focus on symptom relief to the individual patient.
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A novel, fast and efficient single-sensor automatic sleep-stage classification based on complementary cross-frequency coupling estimates. Clin Neurophysiol 2018; 129:815-828. [DOI: 10.1016/j.clinph.2017.12.039] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 11/21/2017] [Accepted: 12/21/2017] [Indexed: 01/18/2023]
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Talboom JS, Huentelman MJ. Big data collision: the internet of things, wearable devices and genomics in the study of neurological traits and disease. Hum Mol Genet 2018; 27:R35-R39. [DOI: 10.1093/hmg/ddy092] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Accepted: 03/12/2018] [Indexed: 12/19/2022] Open
Affiliation(s)
- Joshua S Talboom
- Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA
- Arizona Alzheimer’s Consortium, Phoenix, AZ 85004, USA
| | - Matthew J Huentelman
- Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, AZ 85004, USA
- Arizona Alzheimer’s Consortium, Phoenix, AZ 85004, USA
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Fallahzadeh R, Ghasemzadeh H, Shahrokni A. Electronic Assessment of Physical Decline in Geriatric Cancer Patients. Curr Oncol Rep 2018; 20:26. [PMID: 29516212 PMCID: PMC7412116 DOI: 10.1007/s11912-018-0670-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
PURPOSE OF REVIEW The purpose of this review is to explore state-of-the-art remote monitoring and emerging new sensing technologies for in-home physical assessment and their application/potential in cancer care. In addition, we discuss the main functional and non-functional requirements and research challenges of employing such technologies in real-world settings. RECENT FINDINGS With rapid growth in aging population, effective and efficient patient care has become an important topic. Advances in remote monitoring and in its forefront in-home physical assessment technologies play a fundamental role in reducing the cost and improving the quality of care by complementing the traditional in-clinic healthcare. However, there is a gap in medical research community regarding the applicability and potential outcomes of such systems. While some studies reported positive outcomes using remote assessment technologies, such as web/smart phone-based self-reports and wearable sensors, the cancer research community is still lacking far behind. Thorough investigation of more advanced technologies in cancer care is warranted.
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Affiliation(s)
- Ramin Fallahzadeh
- School of Electrical Engineering and Computer Science, Washington State University, 305 NE Spokane Street, DANA 118A, Pullman, WA, 99164-2752, USA
| | - Hassan Ghasemzadeh
- School of Electrical Engineering and Computer Science, Washington State University, 355 Spokane Street, EME 131, Pullman, WA, 99164-2752, USA
| | - Armin Shahrokni
- Geriatric Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, Box 205, 1275 York Ave., New York, NY, 10065, USA.
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Alqurashi YD, Nakamura T, Goverdovsky V, Moss J, Polkey MI, Mandic DP, Morrell MJ. A novel in-ear sensor to determine sleep latency during the Multiple Sleep Latency Test in healthy adults with and without sleep restriction. Nat Sci Sleep 2018; 10:385-396. [PMID: 30538591 PMCID: PMC6251456 DOI: 10.2147/nss.s175998] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES Detecting sleep latency during the Multiple Sleep Latency Test (MSLT) using electroencephalogram (scalp-EEG) is time-consuming. The aim of this study was to evaluate the efficacy of a novel in-ear sensor (in-ear EEG) to detect the sleep latency, compared to scalp-EEG, during MSLT in healthy adults, with and without sleep restriction. METHODS We recruited 25 healthy adults (28.5±5.3 years) who participated in two MSLTs with simultaneous recording of scalp and in-ear EEG. Each test followed a randomly assigned sleep restriction (≤5 hours sleep) or usual night sleep (≥7 hours sleep). Reaction time and Stroop test were used to assess the functional impact of the sleep restriction. The EEGs were scored blind to the mode of measurement and study conditions, using American Academy of Sleep Medicine 2012 criteria. The Agreement between the scalp and in-ear EEG was assessed using Bland-Altman analysis. RESULTS Technically acceptable data were obtained from 23 adults during 69 out of 92 naps in the sleep restriction condition and 25 adults during 85 out of 100 naps in the usual night sleep. Meaningful sleep restrictions were confirmed by an increase in the reaction time (mean ± SD: 238±30 ms vs 228±27 ms; P=0.045). In the sleep restriction condition, the in-ear EEG exhibited a sensitivity of 0.93 and specificity of 0.80 for detecting sleep latency, with a substantial agreement (κ=0.71), whereas after the usual night's sleep, the in-ear EEG exhibited a sensitivity of 0.91 and specificity of 0.89, again with a substantial agreement (κ=0.79). CONCLUSION The in-ear sensor was able to detect reduced sleep latency following sleep restriction, which was sufficient to impair both the reaction time and cognitive function. Substantial agreement was observed between the scalp and in-ear EEG when measuring sleep latency. This new in-ear EEG technology is shown to have a significant value as a convenient measure for sleep latency.
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Affiliation(s)
- Yousef D Alqurashi
- Sleep and Ventilation Unit, Royal Brompton Campus, National Heart and Lung Institute, Imperial College, London, UK, .,Respiratory Care Department, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia,
| | - Takashi Nakamura
- Department of Electrical and Electronic Engineering, Communications and Signal Processing Group, Imperial College, London, UK
| | - Valentin Goverdovsky
- Department of Electrical and Electronic Engineering, Communications and Signal Processing Group, Imperial College, London, UK
| | - James Moss
- Sleep and Ventilation Unit, Royal Brompton Campus, National Heart and Lung Institute, Imperial College, London, UK,
| | - Michael I Polkey
- National Institute for Health Research, Respiratory Disease Biomedical Research Unit at the Royal Brompton and Harefield National Health Service Foundation Trust and Imperial College, London, UK
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Communications and Signal Processing Group, Imperial College, London, UK
| | - Mary J Morrell
- Sleep and Ventilation Unit, Royal Brompton Campus, National Heart and Lung Institute, Imperial College, London, UK, .,National Institute for Health Research, Respiratory Disease Biomedical Research Unit at the Royal Brompton and Harefield National Health Service Foundation Trust and Imperial College, London, UK
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Malhotra A, Morrell MJ, Eastwood PR. Update in respiratory sleep disorders: Epilogue to a modern review series. Respirology 2018; 23:16-17. [PMID: 29110381 PMCID: PMC5802401 DOI: 10.1111/resp.13211] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Accepted: 09/20/2017] [Indexed: 12/18/2022]
Affiliation(s)
- Atul Malhotra
- Division of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego, La Jolla, California, USA
| | - Mary J Morrell
- National Heart and Lung Institute, Imperial College London, London, UK
- Academic Unit of Sleep and Breathing, Royal Brompton Hospital, London, UK
| | - Peter R Eastwood
- West Australian Sleep Disorders Research Institute, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- Centre for Sleep Science, School of Anatomy, Physiology and Human Biology, University of Western Australia, Perth, Western Australia, Australia
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Mikkelsen KB, Villadsen DB, Otto M, Kidmose P. Automatic sleep staging using ear-EEG. Biomed Eng Online 2017; 16:111. [PMID: 28927417 PMCID: PMC5606130 DOI: 10.1186/s12938-017-0400-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Accepted: 09/02/2017] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Sleep and sleep quality assessment by means of sleep stage analysis is important for both scientific and clinical applications. Unfortunately, the presently preferred method, polysomnography (PSG), requires considerable expert assistance and significantly affects the sleep of the person under observation. A reliable, accurate and mobile alternative to the PSG would make sleep information much more readily available in a wide range of medical circumstances. NEW METHOD Using an already proven method, ear-EEG, in which electrodes are placed inside the concha and ear canal, we measure cerebral activity and automatically score the sleep into up to five stages. These results are compared to manual scoring by trained clinicians, based on a simultaneously recorded PSG. RESULTS The correspondence between manually scored sleep, based on the PSG, and the automatic labelling, based on ear-EEG data, was evaluated using Cohen's kappa coefficient. Kappa values are in the range 0.5-0.8, making ear-EEG relevant for both scientific and clinical applications. Furthermore, a sleep-wake classifier with leave-one-out cross validation yielded specificity of 0.94 and sensitivity of 0.52 for the sleep stage. COMPARISON WITH EXISTING METHOD(S) Ear-EEG based scoring has clear advantages when compared to both the PSG and other mobile solutions, such as actigraphs. It is far more mobile, and potentially cheaper than the PSG, and the information on sleep stages is far superior to a wrist-based actigraph, or other devices based solely on body movement. CONCLUSIONS This study shows that ear-EEG recordings carry information about sleep stages, and indicates that automatic sleep staging based on ear-EEG can classify sleep stages with a level of accuracy that makes it relevant for both scientific and clinical sleep assessment.
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Affiliation(s)
- Kaare B. Mikkelsen
- Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
| | - David Bové Villadsen
- Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
| | - Marit Otto
- Department of Clinical Medicine, Aarhus University, Nørrebrogade 44, 8000 Aarhus C, Denmark
| | - Preben Kidmose
- Department of Engineering, Aarhus University, Finlandsgade 22, 8200 Aarhus N, Denmark
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Goverdovsky V, von Rosenberg W, Nakamura T, Looney D, Sharp DJ, Papavassiliou C, Morrell MJ, Mandic DP. Hearables: Multimodal physiological in-ear sensing. Sci Rep 2017; 7:6948. [PMID: 28761162 PMCID: PMC5537365 DOI: 10.1038/s41598-017-06925-2] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 06/05/2017] [Indexed: 11/09/2022] Open
Abstract
Future health systems require the means to assess and track the neural and physiological function of a user over long periods of time, and in the community. Human body responses are manifested through multiple, interacting modalities – the mechanical, electrical and chemical; yet, current physiological monitors (e.g. actigraphy, heart rate) largely lack in cross-modal ability, are inconvenient and/or stigmatizing. We address these challenges through an inconspicuous earpiece, which benefits from the relatively stable position of the ear canal with respect to vital organs. Equipped with miniature multimodal sensors, it robustly measures the brain, cardiac and respiratory functions. Comprehensive experiments validate each modality within the proposed earpiece, while its potential in wearable health monitoring is illustrated through case studies spanning these three functions. We further demonstrate how combining data from multiple sensors within such an integrated wearable device improves both the accuracy of measurements and the ability to deal with artifacts in real-world scenarios.
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Affiliation(s)
- Valentin Goverdovsky
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2BT, United Kingdom
| | - Wilhelm von Rosenberg
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2BT, United Kingdom
| | - Takashi Nakamura
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2BT, United Kingdom
| | - David Looney
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2BT, United Kingdom
| | - David J Sharp
- Computational, Cognitive, and Clinical Neuroimaging Laboratory, Centre for Neuroscience, Division of Brain Sciences, Imperial College London, London, W12 0NN, United Kingdom
| | - Christos Papavassiliou
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2BT, United Kingdom
| | - Mary J Morrell
- Academic Unit of Sleep and Ventilation, National Heart and Lung Institute, Imperial College London, London, United Kingdom.,NIHR Respiratory Disease Biomedical Research Unit at the Royal Brompton and Harefield NHS Foundation Trust and Imperial College London, London, SW3 6NP, United Kingdom
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2BT, United Kingdom.
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Nakamura T, Goverdovsky V, Morrell MJ, Mandic DP. Automatic Sleep Monitoring Using Ear-EEG. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2017; 5:2800108. [PMID: 29018638 PMCID: PMC5515509 DOI: 10.1109/jtehm.2017.2702558] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 03/03/2017] [Accepted: 04/24/2017] [Indexed: 11/08/2022]
Abstract
The monitoring of sleep patterns without patient's inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency and multi-scale fuzzy entropy, a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings; and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5% to 95.2% for ear-EEG labels predicted from ear-EEG, and 76.8% to 91.8% for scalp-EEG labels predicted from ear-EEG. The corresponding Kappa coefficients range from 0.64 to 0.83 for Scenario 1, and indicate substantial to almost perfect agreement, while for Scenario 2 the range of 0.65-0.80 indicates substantial agreement, thus further supporting the feasibility of in-ear sensing for sleep monitoring in the community.
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Affiliation(s)
- Takashi Nakamura
- Department of Electrical and Electronic EngineeringImperial College London
| | | | - Mary J Morrell
- Sleep and Ventilation UnitNational Heart and Lung Institute, Imperial College London.,NIHR Respiratory Disease Biomedical Research UnitRoyal Brompton and Harefield NHS Foundation Trust, Imperial College London.,Imperial College London
| | - Danilo P Mandic
- Department of Electrical and Electronic EngineeringImperial College London
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