{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:44:38Z","timestamp":1760240678888,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,8,20]],"date-time":"2019-08-20T00:00:00Z","timestamp":1566259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National key research and development program","doi-asserted-by":"publisher","award":["2017YFC0822203"],"award-info":[{"award-number":["2017YFC0822203"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2013AR 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\u2013ECG 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.<\/jats:p>","DOI":"10.3390\/e21080812","type":"journal-article","created":{"date-parts":[[2019,8,21]],"date-time":"2019-08-21T11:19:06Z","timestamp":1566386346000},"page":"812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4512-167X","authenticated-orcid":false,"given":"Shiliang","family":"Shao","sequence":"first","affiliation":[{"name":"School of computer science and engineering, Northeastern University, Shenyang 110819, China"},{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China"}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China"}]},{"given":"Chunhe","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics, Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China"}]},{"given":"Xingchi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of computer science and engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Enuo","family":"Cui","sequence":"additional","affiliation":[{"name":"School of computer science and engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Hai","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of computer science and engineering, Northeastern University, Shenyang 110819, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,20]]},"reference":[{"key":"ref_1","first-page":"e516","article-title":"Heart rate detrended fluctuation indexes as estimate of obstructive sleep apnea severity","volume":"94","author":"Silva","year":"2015","journal-title":"Medince"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TBME.2015.2498199","article-title":"An obstructive sleep apnea detection approach using a discriminative hidden markov model from ECG signals","volume":"63","author":"Song","year":"2016","journal-title":"IEEE Trans. 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