{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:07:36Z","timestamp":1761646056179,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,1,25]],"date-time":"2017-01-25T00:00:00Z","timestamp":1485302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Jiangsu Province of China","award":["BK20160697"],"award-info":[{"award-number":["BK20160697"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571113"],"award-info":[{"award-number":["61571113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Computer aided diagnosis systems can help to reduce the high mortality rate among cardiac patients. Automatical classification of electrocardiogram (ECG) beats plays an important role in such systems, but this issue is challenging because of the complexities of ECG signals. In literature, feature designing has been broadly-studied. However, such methodology is inevitably limited by the heuristics of hand-crafting process and the challenge of signals themselves. To address it, we treat the problem of ECG beat classification from the metric and measurement perspective. We propose a novel approach, named \u201cSet-Based Discriminative Measure\u201d, which first learns a discriminative metric space to ensure that intra-class distances are smaller than inter-class distances for ECG features in a global way, and then measures a new set-based dissimilarity in such learned space to cope with the local variation of samples. Experimental results have demonstrated the advantage of this approach in terms of effectiveness, robustness, and flexibility based on ECG beats from the MIT-BIH Arrhythmia Database.<\/jats:p>","DOI":"10.3390\/s17020234","type":"journal-article","created":{"date-parts":[[2017,1,25]],"date-time":"2017-01-25T09:50:44Z","timestamp":1485337844000},"page":"234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Set-Based Discriminative Measure for Electrocardiogram Beat Classification"],"prefix":"10.3390","volume":"17","author":[{"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, 2 Sipailou, Nanjing 210096, China"}]},{"given":"Jianqing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, 2 Sipailou, Nanjing 210096, China"},{"name":"School of Basic Medical Sciences, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China"}]},{"given":"Qin","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, 2 Sipailou, Nanjing 210096, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.cmpb.2015.12.008","article-title":"ECG-based Heartbeat Classification for Arrhythmia Detection: A Survey","volume":"127","author":"Luz","year":"2016","journal-title":"Comput. 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