{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:29:50Z","timestamp":1760243390343,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2013,1,9]],"date-time":"2013-01-09T00:00:00Z","timestamp":1357689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.<\/jats:p>","DOI":"10.3390\/s130100813","type":"journal-article","created":{"date-parts":[[2013,1,9]],"date-time":"2013-01-09T12:43:46Z","timestamp":1357735426000},"page":"813-828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System"],"prefix":"10.3390","volume":"13","author":[{"given":"Shing-Hong","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Da-Chuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih-Ming","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2013,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6799","DOI":"10.3390\/s110706799","article-title":"A comprehensive ubiquitous healthcare solution on an android mobile device","volume":"11","author":"Hii","year":"2011","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/10.43620","article-title":"A comparison of the noise sensitivity of nine qrs detection algorithms","volume":"37","author":"Friesen","year":"1990","journal-title":"IEEE Trans. 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