Neural network and principal component analyses of highly variable myocardial mechanical waveforms derived from echocardiographic ultrasound images

Abstract

We introduce a new type of data for classification of regional segments of myocardium. We have analyzed strain measurements taken throughout the cardiac cycle from the echocardiograms of pigs. Classifications by both principal component analysis (PCA) and by neural network (NN) are combined for a data mining operation. Differences in strain waveforms between normal and diseased myocardium may further elucidate the corresponding changes in physiology. Altered functioning of the heart muscle is reflected by strain, and objective computer analysis should aid in the diagnosis of ischemia. We hypothesize that the entire strain waveform over one heart cycle can be classified to functionally determine whether or not a myocardial region is perfused.