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Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep\u2010monitoring model based on single\u2010channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short\u2010term memory (LSTM) is used to learn the long\u2010term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10\u2009s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen\u2019s kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea\u2010ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single\u2010lead ECG.<\/jats:p>","DOI":"10.1155\/2021\/5594733","type":"journal-article","created":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T20:36:35Z","timestamp":1616531795000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN\u2010LSTM Model"],"prefix":"10.1155","volume":"2021","author":[{"given":"Junming","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9975-6745","authenticated-orcid":false,"given":"Jinfeng","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiliang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1314-528X","authenticated-orcid":false,"given":"Ruxian","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,3,23]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1093\/sleep\/20.9.705"},{"key":"e_1_2_9_2_2","first-page":"17","volume-title":"The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications","author":"Iber C.","year":"2007"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1136\/thx.2003.015867"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tbme.2016.2554138"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1378\/chest.123.4.1134"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2405075"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1164\/ajrccm\/148.3.618"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.01587-2017"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2016.2636778"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.06.028"},{"key":"e_1_2_9_11_2","first-page":"753","article-title":"Detection of obstructive sleep apnea from cardiac interbeat interval time series","volume":"27","author":"Mietus J. 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