Wavelet based event detection in the phonocardiogram of prolapsed mitral valve.

Mitral valve prolapse (MVP) is one of the cardiovascular valve abnormalities that occurs due to the stretching of mitral valve leaflets, which develops in around 2 percent of the population. MVP is usually detected via auscultation and diagnosed with an echocardiogram, which is an expensive procedure. The characteristic auscultatory finding in MVP is a mid-to-late systolic click which is usually followed by a high-pitched systolic murmur. These can be easily detected on a phonocardiogram which is a graphical representation of the auscultatory signal. In this paper, we have proposed a method to automatically identify patterns in the PCG that can help in diagnosing MVP as well as monitor its progression into Mitral Regurgitation. In the proposed methodology the systolic part, which is the region of interest here, is isolated by preprocessing and thresholded Teager-Kaiser energy envelope of the signal. Scalogram images of the systole part are obtained by applying continuous wavelet transform. These scalograms are used to train the convolutional neural network (CNN). A two-layer CNN could identify the event patterns with nearly 100% accuracy on the test dataset with varying sizes (20% - 40% of the entire data). The proposed method shows potential in the quick screening of MVP patients.