Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal - 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 (Knowledge-based systems, 2017)
•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 Undiagnosed coronary artery disease (CAD) progresses rapidly and leads to myocardial infarction (MI) by reducing the blood flow to the cardiac muscles. Timely diagnosis of MI and its location is significant, else, it expands and may impair the left ventricular (LV) function. Thus, if CAD and MI are not picked up by electrocardiogram (ECG) during diagnostic test, it can lead to congestive heart failure (CHF). Therefore, in this paper, the characterization of three cardiac abnormalities namely, CAD, MI and CHF are compared. Performance of novel algorithms is based on contourlet and shearlet transformations of the ECG signals. Continuous wavelet transform (CWT) is performed on normal, CAD, MI and CHF ECG beat to obtain scalograms. Subsequently, contourlet and shearlet transformations are applied on the scalograms to obtain the respective coefficients. Entropies, first and second order statistical features namely, mean (Mni), min (Mini), max (Mxi), standard deviation (Dsti), average power (Pavgi), inter-quartile range (IQRi), Shannon entropy (Eshi), mean Tsallis entropy (Emtsi), kurtosis (Kuri), mean absolute deviation (MADi), and mean energy (Ωmi), are extracted from each contourlet and shearlet coefficients. Only significant features are selected using improved binary particle swarm optimization (IBPSO) feature selection method. Selected features are ranked using analysis of variance (ANOVA) and relieff techniques. The highly ranked features are subjected to decision tree (DT) and K-nearest neighbor (KNN) classifiers. Proposed method has achieved accuracy, sensitivity and specificity of (i) 99.55%, 99.93% and 99.24% using contourlet transform, and (ii) 99.01%, 99.82% and 98.75% using shearlet transform. Among the two proposed techniques, contourlet transform method performed marginally better than shearlet transform technique in classifying the four classes. The proposed CWT combined with contourlet-based technique can be implemented in hospitals to speed up the diagnosis of three different cardiac abnormalities using a single ECG test. This technique, minimizes the unnecessary diagnostic tests required to confirm the diagnosis.