Classification of sleep apnea based on EEG sub-band signal characteristics

Abstract Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed...

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Autores principales:	Xiaoyun Zhao, Xiaohong Wang, Tianshun Yang, Siyu Ji, Huiquan Wang, Jinhai Wang, Yao Wang, Qi Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5150c1a3c0114271a2be02037537845d
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spelling	oai:doaj.org-article:5150c1a3c0114271a2be02037537845d2021-12-02T13:30:37ZClassification of sleep apnea based on EEG sub-band signal characteristics10.1038/s41598-021-85138-02045-2322https://doaj.org/article/5150c1a3c0114271a2be02037537845d2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85138-0https://doaj.org/toc/2045-2322Abstract Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.Xiaoyun ZhaoXiaohong WangTianshun YangSiyu JiHuiquan WangJinhai WangYao WangQi WuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xiaoyun Zhao Xiaohong Wang Tianshun Yang Siyu Ji Huiquan Wang Jinhai Wang Yao Wang Qi Wu Classification of sleep apnea based on EEG sub-band signal characteristics
description	Abstract Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.
format	article
author	Xiaoyun Zhao Xiaohong Wang Tianshun Yang Siyu Ji Huiquan Wang Jinhai Wang Yao Wang Qi Wu
author_facet	Xiaoyun Zhao Xiaohong Wang Tianshun Yang Siyu Ji Huiquan Wang Jinhai Wang Yao Wang Qi Wu
author_sort	Xiaoyun Zhao
title	Classification of sleep apnea based on EEG sub-band signal characteristics
title_short	Classification of sleep apnea based on EEG sub-band signal characteristics
title_full	Classification of sleep apnea based on EEG sub-band signal characteristics
title_fullStr	Classification of sleep apnea based on EEG sub-band signal characteristics
title_full_unstemmed	Classification of sleep apnea based on EEG sub-band signal characteristics
title_sort	classification of sleep apnea based on eeg sub-band signal characteristics
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5150c1a3c0114271a2be02037537845d
work_keys_str_mv	AT xiaoyunzhao classificationofsleepapneabasedoneegsubbandsignalcharacteristics AT xiaohongwang classificationofsleepapneabasedoneegsubbandsignalcharacteristics AT tianshunyang classificationofsleepapneabasedoneegsubbandsignalcharacteristics AT siyuji classificationofsleepapneabasedoneegsubbandsignalcharacteristics AT huiquanwang classificationofsleepapneabasedoneegsubbandsignalcharacteristics AT jinhaiwang classificationofsleepapneabasedoneegsubbandsignalcharacteristics AT yaowang classificationofsleepapneabasedoneegsubbandsignalcharacteristics AT qiwu classificationofsleepapneabasedoneegsubbandsignalcharacteristics
_version_	1718392913913708544

Classification of sleep apnea based on EEG sub-band signal characteristics

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