| 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
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| Department | 机器人学研究室 |
| Corresponding Author | Shao, Shiliang(shaoshiliang@sia.cn) |
| Source Publication | ENTROPY
 |
| ISSN | 1099-4300 |
| 2019 | |
| Volume | 21Issue:8Pages:1-14 |
| Indexed By | SCI |
| WOS ID | WOS:000483732700026 |
| Contribution Rank | 1 |
| Funding Organization | National key research and development program of China [2016YFE0206200, 2017YFC0822203] |
| Keyword | heart rate variability obstruct sleep apnea power spectrum Shannon entropy |
| Abstract | 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. |
| Language | 英语 |
| WOS Subject | Physics, Multidisciplinary |
| WOS Keyword | FREQUENCY-DOMAIN ; AUTOREGRESSIVE MODELS |
| WOS Research Area | Physics |
| Funding Project | National 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 |
| Identifier | http://ir.sia.cn/handle/173321/25622 |
| Collection | 机器人学研究室 |
| Corresponding Author | Shao SL(邵士亮) |
| Affiliation | 1.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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Obstructive Sleep Ap（2990KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
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