Synchrosqueezed Transform based Click Event Segmentation in Phonocardiogram of Mitral Valve Prolapse.

Phonocardiogram (PCG) signal of the mitral valve prolapse (MVP) patients is characterized by transient audio events which include a systolic click (SC) followed by a murmur of varying intensity. Physicians detect these auscultation clues in regular auscultation before ordering expensive echocardio-graphy test. But auscultation is often error prone and even physicians with considerable experience might end up missing these clues. Therefore developing machine learning techniques to help clinicians is the need of the hour. A segmentation technique using Fourier synchrosqueezed transform (FSST) features with a long short term memory (LSTM) network is proposed in this study. An accuracy of 99.8% on MVP dataset demonstrates the potential of the proposed method in clinical diagnosis.

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.