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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Veröffentlicht in:	Knowledge-based systems Jg. 99; S. 146 - 156
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.05.2016
Schlagworte:	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-Zugang:	Volltext
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