{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T18:32:27Z","timestamp":1784140347857,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T00:00:00Z","timestamp":1727568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the key research and development plan in Ningxia province","award":["2023BEG02065"],"award-info":[{"award-number":["2023BEG02065"]}]},{"name":"Innovation Team of Lidar Atmosphere Remote Sensing of Ningxia Province","award":["2023BEG02065"],"award-info":[{"award-number":["2023BEG02065"]}]},{"name":"the high level talent selection and training plan of North Minzu University","award":["2023BEG02065"],"award-info":[{"award-number":["2023BEG02065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention mechanism (SE module), convolutional neural network (CNN), and long short-term memory neural network (LSTM). The data of this model use the MIT-BIH arrhythmia database, and after noise reduction of raw ECG data by the EEMD denoising algorithm, a CNN-LSTM is used to learn features from the data, and the fusion channel attention mechanism is used to adjust the weight of the feature map. The CNN-LSTM-SE model is compared with the LSTM, CNN-LSTM, and LSTM-attention models, and the models are evaluated using Precision, Recall, and F1-Score. The classification performance of the tested CNN-LSTM-SE classification prediction model is better, with a classification accuracy of 98.5%, a classification precision rate of more than 97% for each label, a recall rate of more than 98%, and an F1-score of more than 0.98. It meets the requirements of arrhythmia classification prediction and has a certain practical value.<\/jats:p>","DOI":"10.3390\/s24196306","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T07:19:37Z","timestamp":1727680777000},"page":"6306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm"],"prefix":"10.3390","volume":"24","author":[{"given":"Ao","family":"Sun","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, North Minzu University, North Wenchang Road, Yinchuan 750021, China"},{"name":"Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, North Wenchang Road, Yinchuan 750021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, North Minzu University, North Wenchang Road, Yinchuan 750021, China"},{"name":"Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, North Wenchang Road, Yinchuan 750021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, North Minzu University, North Wenchang Road, Yinchuan 750021, China"},{"name":"Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, North Wenchang Road, Yinchuan 750021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7381-4476","authenticated-orcid":false,"given":"Jiandong","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, North Minzu University, North Wenchang Road, Yinchuan 750021, China"},{"name":"Key Laboratory of Atmospheric Environment Remote Sensing of Ningxia, North Wenchang Road, Yinchuan 750021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bhat, T., Bhat, A., Acharya, S., Bhat, S., and Taleka, M. 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