Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal

•Classification of normal, CAD, MI and CHF classes using ECG beat is proposed•CWT transform is performed on ECG beat•Contourlet and shearlet transforms are performed on scalogram•First and second order statistical features are extracted•Obtained an accuracy of 99.55% using contourlet transform Undia...

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Veröffentlicht in:	Knowledge-based systems Jg. 132; S. 156 - 166
Hauptverfasser:	Acharya, U Rajendra, Fujita, Hamido, Sudarshan, Vidya K, Oh, Shu Lih, Adam, Muhammad, Tan, Jen Hong, Koo, Jie Hui, Jain, Arihant, Lim, Choo Min, Chua, Kuang Chua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 15.09.2017 Elsevier Science Ltd
Schlagworte:	Abnormalities Algorithms Automatic classification Averages Blood flow Cardiovascular disease Classifiers Congestive heart failure Continuous wavelet transform Contourlet and shearlet transforms Coronary artery disease Decision making Deviation Diagnostic systems Diagnostic tests Disease Echocardiography Electrocardiogram Electrocardiography Entropy Entropy (Information theory) Feature extraction Function words Heart failure Hospitals Kurtosis Muscles Myocardial infarction Optimization Particle swarm optimization Ultrasonic imaging Undiagnosed Variance analysis Myocardial infarction Contourlet and shearlet transforms Congestive heart failure Coronary artery disease Electrocardiogram Continuous wavelet transform
ISSN:	0950-7051, 1872-7409
Online-Zugang:	Volltext
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