Detection and diagnosis of dilated cardiomyopathy from the left ventricular parameters in echocardiogram sequences Online publication date: Wed, 23-Oct-2019
by G.N. Balaji; T.S. Subashini; A. Suresh; M.S. Prashanth
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 31, No. 4, 2019
Abstract: The heart has a complicated anatomy and is in constant movement. The cardiologist use echocardiogram to visualise the anatomy and its movement. It is difficult for the cardiologist to prognosticate or affirm the diseases like heart muscle damage, valvular problems, etc. due to presence of less information in echocardiograms. In this paper a system is proposed which automatically segments the left ventricle from the given echocardiogram video sequences using the combination of fuzzy C-means clustering and morphological operations and from which the left ventricle parameters and shape features are evoked. These features are then employed to linear discriminant analysis, K-nearest neighbour and Hopfield neural network to determine whether the heart is normal or affected with DCM. With LV parameters evaluated and shape features extracted it was found that HNN was able to model normal and abnormal hearts very well with an accuracy of 88% compared to LDA and K-NN.
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