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Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability
Shao SL(邵士亮)1,2,3; Wang T(王挺)2,3; Song CH(宋纯贺)2,3; Chen, Xingchi1; Cui EN( 崔婀娜)1; Zhao H(赵海)1
Department机器人学研究室
Corresponding AuthorShao, Shiliang(shaoshiliang@sia.cn)
Source PublicationENTROPY
ISSN1099-4300
2019
Volume21Issue:8Pages:1-14
Indexed BySCI
WOS IDWOS:000483732700026
Contribution Rank1
Funding OrganizationNational key research and development program of China [2016YFE0206200, 2017YFC0822203]
Keywordheart rate variability obstruct sleep apnea power spectrum Shannon entropy
AbstractObstructive 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.
Language英语
WOS SubjectPhysics, Multidisciplinary
WOS KeywordFREQUENCY-DOMAIN ; AUTOREGRESSIVE MODELS
WOS Research AreaPhysics
Funding ProjectNational key research and development program of China[2016YFE0206200] ; National key research and development program of China[2017YFC0822203]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25622
Collection机器人学研究室
Corresponding AuthorShao SL(邵士亮)
Affiliation1.School of computer science and engineering, Northeastern University, Shenyang 110819, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and IntelligentManufacturing, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Shao SL,Wang T,Song CH,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 Shao SL,Wang T,Song CH,Chen, Xingchi,Cui EN,&Zhao H.(2019).Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability.ENTROPY,21(8),1-14.
MLA Shao SL,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.
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