Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads

Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction (MI). However, manual annotation of this complex nonlinear ECG signal is not only cumbersome and time consuming but also inaccurate. Hence,...

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Bibliographic Details
Published in:	Knowledge-based systems Vol. 99; pp. 146 - 156
Main Authors:	Acharya, U. Rajendra, Fujita, Hamido, Sudarshan, Vidya K., Oh, Shu Lih, Adam, Muhammad, Koh, Joel E.W., Tan, Jen Hong, Ghista, Dhanjoo N., Martis, Roshan Joy, Chua, Chua K., Poo, Chua Kok, Tan, Ru San
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.05.2016
Subjects:	Automation Classification Classifier Computer programs Discrete wavelet transform Electrocardiogram Entropy Myocardial infarction Nonlinearity Position (location) Software Myocardial infarction Entropy Discrete wavelet transform Classifier Electrocardiogram
ISSN:	0950-7051, 1872-7409
Online Access:	Get full text
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