{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:29:42Z","timestamp":1762608582314,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T00:00:00Z","timestamp":1642377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology Taiwan","award":["MOST 109-2121-M-039-001","MOST 109-2813-C-039-018-E","MOST 110-2121-M-039-001"],"award-info":[{"award-number":["MOST 109-2121-M-039-001","MOST 109-2813-C-039-018-E","MOST 110-2121-M-039-001"]}]},{"DOI":"10.13039\/501100012544","name":"China Medical University, Taiwan","doi-asserted-by":"publisher","award":["CMU110-MF-111"],"award-info":[{"award-number":["CMU110-MF-111"]}],"id":[{"id":"10.13039\/501100012544","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We established a web-based ubiquitous health management (UHM) system, \u201cECG4UHM\u201d, for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert\u2013Huang transform (HHT) with empirical mode decomposition to calculate ECGs\u2019 intrinsic mode functions (IMFs). The area centroids of the IMFs\u2019 marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLABTM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC\u2013VPC) and the fibril-rapid pattern (i.e., AFib\u2013VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC\u2013VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.<\/jats:p>","DOI":"10.3390\/s22020689","type":"journal-article","created":{"date-parts":[[2022,1,17]],"date-time":"2022-01-17T20:49:21Z","timestamp":1642452561000},"page":"689","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management"],"prefix":"10.3390","volume":"22","author":[{"given":"Wei-Ting","family":"Hsiao","sequence":"first","affiliation":[{"name":"Department and Institute of Health Service Administrations, China Medical University, Taichung 406040, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3611-5067","authenticated-orcid":false,"given":"Yao-Chiang","family":"Kan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chin-Chi","family":"Kuo","sequence":"additional","affiliation":[{"name":"Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, College of Medicine, China Medical University, Taichung 40402, Taiwan"},{"name":"Big Data Center, China Medical University Hospital, Taichung 40402, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Chieh","family":"Kuo","sequence":"additional","affiliation":[{"name":"Department and Institute of Health Service Administrations, China Medical University, Taichung 406040, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sin-Kuo","family":"Chai","sequence":"additional","affiliation":[{"name":"Department and Institute of Health Service Administrations, China Medical University, Taichung 406040, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6258-8124","authenticated-orcid":false,"given":"Hsueh-Chun","family":"Lin","sequence":"additional","affiliation":[{"name":"Department and Institute of Health Service Administrations, China Medical University, Taichung 406040, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.ijmedinf.2019.03.018","article-title":"Methods of usability testing in the development of eHealth applications: A scoping review","volume":"126","author":"Maramba","year":"2019","journal-title":"Int. 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