{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T06:12:03Z","timestamp":1759385523613,"version":"3.40.5"},"reference-count":66,"publisher":"Walter de Gruyter GmbH","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Myocardial infarction (MI) happens when blood stops circulating to an explicit segment of the heart causing harm to the heart muscles. Vectorcardiography (VCG) is a technique of recording direction and magnitude of the signals that are produced by the heart in a 3-lead representation. In this work, we present a technique for detection of MI in the inferior portion of heart using short duration VCG signals. The raw signal was pre-processed using the median and Savitzky\u2013Golay (SG) filter. The Stationary Wavelet Transform (SWT) was used for time-invariant decomposition of the signal followed by feature extraction. The selected features using minimum-redundancy-maximum-relevance (mRMR) based feature selection method were applied to the supervised classification methods. The efficacy of the proposed method was assessed under both class-oriented and a more real-life subject-oriented approach. An accuracy of 99.14 and 89.37% were achieved respectively. Results of the proposed technique are better than existing state-of-art methods and used VCG segment is shorter. Thus, a shorter segment and a high accuracy can be helpful in the automation of timely and reliable detection of MI. The satisfactory performance achieved in the subject-oriented approach shows reliability and applicability of the proposed technique.<\/jats:p>","DOI":"10.1515\/bmt-2020-0329","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T20:51:17Z","timestamp":1620075077000},"page":"489-501","source":"Crossref","is-referenced-by-count":14,"title":["Short duration Vectorcardiogram based inferior myocardial infarction detection: class and subject-oriented approach"],"prefix":"10.1515","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4890-1898","authenticated-orcid":false,"given":"Jagdeep","family":"Rahul","sequence":"first","affiliation":[{"name":"Department of Electronics & Communication Engineering , Rajiv Gandhi University , Itanagar , Arunachal Pradesh , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lakhan Dev","family":"Sharma","sequence":"additional","affiliation":[{"name":"School of Electronics Engineering , VIT-AP University , Amaravati , Andhra Pradesh , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vijay Kumar","family":"Bohat","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering , Bennett University , Greater Noida , Uttar Pradesh , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2021,5,4]]},"reference":[{"key":"2023033112020968412_j_bmt-2020-0329_ref_001","unstructured":"Guyton, AC, Hall, JE. 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