Detection and diagnosis of dilated and hypertrophic cardiomyopathy by echocardiogram sequences analysis

Abstract

Automating the detection and diagnosis of cardiovascular diseases using echocardiogram sequences is a challenging task because of the presence of speckle noise, less information and movement of chambers. In this paper an attempt has been made to classify the normal hearts, and hearts affected by dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) by automating the segmentation of left ventricle (LV). The segmented LV from the diastolic frames of echocardiogram sequences alone is used for extracting features. The statistical features and Zernike moment features are obtained from extracted diastolic LV and classified using the classifiers namely support vector machine (SVM), back propagation neural network (BPNN) and probabilistic neural network (PNN). An intensive examination over 60 echocardiogram sequences reveals that the proposed method performs well in classifying normal hearts and hearts affected by DCM and HCM. Among the classifiers used the BPNN classifier with the combination of Zernike moment features gave an highest accuracy of 92.08 %.