{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:11:02Z","timestamp":1777889462997,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["KES"],"published-print":{"date-parts":[[2020,9,28]]},"abstract":"<jats:p>Many heart diseases can be identified and cured at an early stage by studying the changes in the features of electrocardiogram (ECG) signal. Myocardial Infarction (MI) is the serious cause of death worldwide. If MI can be detected early, the death rate will reduce. In this paper, an algorithm to detect MI in an ECG signal using Daubechies wavelet transform technique is developed. The ECG signal-denoising is performed by removing the corresponding wavelet coefficients at higher scale. After denoising, an important step towards identifying an arrhythmia is the feature extraction from the ECG. Feature extraction is carried out to detect the R peaks of the ECG signal. Since as R peak is having the highest amplitude, and therefore it is detected in the first round, subsequently location of other peaks are determined. Having completed the preprocessing and the feature extraction the MI is detected from the ECG based on inverted T wave logic and ST segment elevation. The algorithm was evaluated using MIT-BIH database and European database satisfactorily.<\/jats:p>","DOI":"10.3233\/kes-200043","type":"journal-article","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T13:57:10Z","timestamp":1601647030000},"page":"217-226","source":"Crossref","is-referenced-by-count":0,"title":["Identification of myocardial infarction from analysis of ECG signal"],"prefix":"10.1177","volume":"24","author":[{"given":"D.B.V.","family":"Jagannadham","sequence":"first","affiliation":[{"name":"Department of ECE, Gayatri Vidya Parishad College of Engineering, Madhurwada, Visakhapatnam, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.V.","family":"Sai Narayana","sequence":"additional","affiliation":[{"name":"Department of ECE, Gayatri Vidya Parishad College of Engineering, Madhurwada, Visakhapatnam, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P.","family":"Ganesh","sequence":"additional","affiliation":[{"name":"Department of ECE, Gayatri Vidya Parishad College of Engineering, Madhurwada, Visakhapatnam, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D.","family":"Koteswar","sequence":"additional","affiliation":[{"name":"Department of Electronic and Telecommunication Engineering, Indian Institute of Engineering Science and Technology, Shibupur, Howrah, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/KES-200043_ref1","unstructured":"M. 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Singh, Adaptive Filter Design for ECG Noise Reduction using LMS Algorithm, in: 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), Noida, 2015, pp. 1\u20136.","DOI":"10.1109\/ICRITO.2015.7359333"},{"issue":"3","key":"10.3233\/KES-200043_ref7","doi-asserted-by":"crossref","first-page":"276","DOI":"10.3844\/ajassp.2008.276.281","article-title":"ECG signal denoising by wavelet transform thresholding","volume":"5","author":"Alfoouri","year":"2008","journal-title":"Am. J. Appl. Sci."},{"key":"10.3233\/KES-200043_ref8","doi-asserted-by":"crossref","unstructured":"T. Yadav, Denoising and SNR Improvement of ECG Signals Using Wavelet Based Techniques, in: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), Dehradun, no. October, 2016, pp. 678\u2013682.","DOI":"10.1109\/NGCT.2016.7877498"},{"key":"10.3233\/KES-200043_ref9","unstructured":"M. Meenakshi, ECG Signal Denoising and Ischemic Event Feature Extraction using ECG Signal Denoising and Ischemic Event Feature Extraction using Daubechies Wavelets, in: IJCA Proceedings on National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2012), no. October, 2016."},{"key":"10.3233\/KES-200043_ref10","doi-asserted-by":"crossref","unstructured":"S. Ramakrishnan, Design and Analysis of Feature Extraction Algorithm for ECG signals using Adaptive Threshold Method, in: 2017 Trends in Industrial Measurement and Automation (TIMA), Chennai, 2017.","DOI":"10.1109\/TIMA.2017.8064801"}],"container-title":["International Journal of Knowledge-based and Intelligent Engineering Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/KES-200043","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:11:11Z","timestamp":1777612271000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/KES-200043"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,28]]},"references-count":10,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/kes-200043","relation":{},"ISSN":["1327-2314","1875-8827"],"issn-type":[{"value":"1327-2314","type":"print"},{"value":"1875-8827","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,28]]}}}