{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T08:11:15Z","timestamp":1767168675854,"version":"build-2238731810"},"reference-count":31,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2021,9,27]]},"abstract":"<jats:p>The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze the cardiovascular status and check health, a standard for detecting and diagnosing abnormal heart rhythms. In recent years, cardiovascular health has attracted much attention. However, traditional doctors\u2019 consultations have disadvantages such as delayed diagnosis and high misdiagnosis rate, while cardiovascular diseases have the characteristics of early diagnosis, early treatment, and early recovery. Therefore, it is essential to reduce the misdiagnosis rate of heart disease. Our work is based on five different types of ECG arrhythmia classified according to the AAMI EC57 standard, namely, nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beat. This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural network (CNN), which could accurately classify ECG signals. We evaluated the classification effect of this classification method on the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) based on the MIT-BIH arrhythmia database. According to the results, the proposed method achieved 99.8% accuracy, 98.4% sensitivity, 99.9% specificity, and 98.5% positive prediction rate for detecting VEB. Detection of SVEB achieved 99.7% accuracy, 92.1% sensitivity, 99.9% specificity, and 96.8% positive prediction rate.<\/jats:p>","DOI":"10.1155\/2021\/7167891","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T19:35:06Z","timestamp":1632771306000},"page":"1-9","source":"Crossref","is-referenced-by-count":15,"title":["An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2332-6081","authenticated-orcid":true,"given":"Dengqing","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Cardiology, Jinjiang Municipal Hospital, Fujian, Jinjiang 362200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4228-6751","authenticated-orcid":true,"given":"Yuxuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Informatics Xiamen University, Xiamen University, Fujian, Xiamen 361000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunyi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Informatics Xiamen University, Xiamen University, Fujian, Xiamen 361000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyi","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Jinjiang Municipal Hospital, Fujian, Jinjiang 362200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyu","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Jinjiang Municipal Hospital, Fujian, Jinjiang 362200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6283-6127","authenticated-orcid":true,"given":"Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Public Health, Jinjiang Municipal Hospital, Fujian, Jinjiang 362200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","volume-title":"World Health Statistics 2019: Monitoring Health for the SDGs, Sustainable Development goals","author":"World Health Organization","year":"2019"},{"key":"2","article-title":"Very deep convolutional networks for large-scale image recognition","author":"K. 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