{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T04:34:16Z","timestamp":1773894856165,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Henan Science and Technology Department","award":["182207310002"],"award-info":[{"award-number":["182207310002"]}]},{"DOI":"10.13039\/501100009967","name":"Xinjiang Production and Construction Corps","doi-asserted-by":"publisher","award":["2018AB017"],"award-info":[{"award-number":["2018AB017"]}],"id":[{"id":"10.13039\/501100009967","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Atrial fibrillation (AF) is an arrhythmia that may cause blood clots and increase the risk of stroke and heart failure. Traditional 12-lead electrocardiogram (ECG) acquisition equipment is complex and difficult to carry. Short single-lead ECG recordings based on wearable devices can remedy these shortcomings. However, reliable and accurate atrial fibrillation detection is still an issue because of the limited information on the short single-lead ECG recordings. In this paper, we propose a novel multi-branch convolutional neural network and bidirectional long short-term memory network (MCNN-BLSTM) to deal with the reliability and accuracy of AF detection in short single-lead ECG recordings. Firstly, to fuller extract the feature information of short single-lead ECG recordings, the MCNN module is designed to dynamically set several corresponding branches according to the number of slices of short single-lead ECG recordings. Then, the BLSTM module is designed to further enhance the feature information learned from each branch. We validated the model on the PhysioNet\/CinC Challenge 2017 (CinC2017) database and verified the generalization of the model on the China Physiological Signal Challenge 2018 (CPSC2018) database. The results show that the accuracy of the model on the CinC 2017 database reaches 87.57%, and the average F1 score reaches 84.56%. The accuracy of the model on the CPSC 2018 database reaches 87.50%, and the average F1 score reaches 82.01%. Compared with other advanced methods, our model shows better performance and can meet the daily needs of atrial fibrillation detection with short ECG wearable devices.<\/jats:p>","DOI":"10.3390\/a15120454","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T08:46:41Z","timestamp":1669798001000},"page":"454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["An Effective Atrial Fibrillation Detection from Short Single-Lead Electrocardiogram Recordings Using MCNN-BLSTM Network"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3485-8470","authenticated-orcid":false,"given":"Hongpo","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"},{"name":"Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450003, China"},{"name":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0587-4706","authenticated-orcid":false,"given":"Hongzhuang","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}]},{"given":"Junli","family":"Gao","sequence":"additional","affiliation":[{"name":"Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450003, China"}]},{"given":"Peng","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Automation, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0016-5177","authenticated-orcid":false,"given":"Guanhe","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}]},{"given":"Zongmin","family":"Wang","sequence":"additional","affiliation":[{"name":"Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450003, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1093\/eurheartj\/ehaa612","article-title":"2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): The Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) Developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC","volume":"42","author":"Hindricks","year":"2020","journal-title":"Eur. 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