{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:50:02Z","timestamp":1782989402466,"version":"3.54.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686684","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,29]]},"abstract":"<jats:p>Myocardial Infarction remains a high-mortality cardiovascular disease. While ST-Elevation Myocardial Infarction (STEMI) is the traditional intervention standard, many Occlusion Myocardial Infarction (OMI) cases are misclassified as Non-ST Elevation Myocardial Infarction (NSTEMI), leading to delayed treatment and poor outcomes. This study collected clinical and 12-lead ECG data from AMI patients over five years, categorized into OMI and Non Occlusion Myocardial Infarction (NOMI) groups based on angiography and cardiac enzymes. We developed a ResNet-1D deep learning model to identify OMI from Electrocardiograms (ECG) signal patterns. The model achieved an OMI recall of 0.75 and specificity to 0.53. Our findings suggest that while ST-elevation remains a primary OMI indicator, machine learning can effectively assist clinicians in detecting hidden OMI cases within the NSTEMI population, providing critical diagnostic support alongside clinical analysis.<\/jats:p>","DOI":"10.3233\/shti260818","type":"book-chapter","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:59:53Z","timestamp":1782986393000},"source":"Crossref","is-referenced-by-count":0,"title":["AI-Based ECG Analysis for Early Detection of Occlusion Myocardial Infarction Using Coronary Angiography Results"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4891-6299","authenticated-orcid":false,"given":"Liong-Rung","family":"Liu","sequence":"first","affiliation":[{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan"},{"name":"Department of Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6919-8199","authenticated-orcid":false,"given":"Hung-wen","family":"Chiu","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Health Sciences Informatics Leads and Empowers the Digital Health Transformation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260818","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:59:54Z","timestamp":1782986394000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,29]]},"ISBN":["9781643686684"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260818","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,29]]}}}