Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability

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	Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability
	Chen, Xingchi 3; Cui EN( 崔婀娜)3; Zhao H(赵海)3; Shao SL(邵士亮)1,2,3 ; Wang T(王挺)1,2; Song CH(宋纯贺)1,2
刊名	ENTROPY
	2019
卷号	21 期号:8 页码:1-14
关键词	heart rate variability obstruct sleep apnea power spectrum Shannon entropy
ISSN号	1099-4300
通讯作者	Shao, Shiliang(shaoshiliang@sia.cn)
产权排序	1
英文摘要	Obstructive sleep apnea (OSA) syndrome is a common sleep disorder. As an alternative to polysomnography (PSG) for OSA screening, the current automatic OSA detection methods mainly concentrate on feature extraction and classifier selection based on physiological signals. It has been reported that OSA is, along with autonomic nervous system (ANS) dysfunction and heart rate variability (HRV), a useful tool for ANS assessment. Therefore, in this paper, eight novel indices of short-time HRV are extracted for OSA detection, which are based on the proposed multi-bands time-frequency spectrum entropy (MTFSE) method. In the MTFSE, firstly, the power spectrum of HRV is estimated by the Burg-AR model, and the time-frequency spectrum image (TFSI) is obtained. Secondly, according to the physiological significance of HRV, the TFSI is divided into multiple sub-bands according to frequency. Last but not least, by studying the Shannon entropy of different sub-bands and the relationships among them, the eight indices are obtained. In order to validate the performance of MTFSE-based indices, the Physionet Apnea-ECG database and K-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT) classification methods are used. The SVM classification method gets the highest classification accuracy, its average accuracy is 91.89%, the average sensitivity is 88.01%, and the average specificity is 93.98%. Undeniably, the MTFSE-based indices provide a novel idea for the screening of OSA disease.
资助项目	National key research and development program of China[2016YFE0206200] ; National key research and development program of China[2017YFC0822203]
WOS关键词	FREQUENCY-DOMAIN ; AUTOREGRESSIVE MODELS
WOS研究方向	Physics
语种	英语
WOS记录号	WOS:000483732700026
资助机构	National key research and development program of China [2016YFE0206200, 2017YFC0822203]
内容类型	期刊论文
源URL	[http://ir.sia.cn/handle/173321/25622]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Shao SL(邵士亮)
作者单位	1.Institutes for Robotics and IntelligentManufacturing, Chinese Academy of Sciences, Shenyang 110016, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China 3.School of computer science and engineering, Northeastern University, Shenyang 110819, China
推荐引用方式 GB/T 7714	Chen, Xingchi,Cui EN,Zhao H,et al. Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability[J]. ENTROPY,2019,21(8):1-14.
APA	Chen, Xingchi,Cui EN,Zhao H,Shao SL,Wang T,&Song CH.(2019).Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability.ENTROPY,21(8),1-14.
MLA	Chen, Xingchi,et al."Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability".ENTROPY 21.8(2019):1-14.