Graph-based complex network features for the auscultation of mitral incompetence

Abstract

The paper unravels the potential of graph theory in the heart murmur auscultation by constructing a complex network from the single murmur time-series signals. In this study, forty-eight murmur signals of mitral incompetence (MI) and healthy heart (NM) are subjected to complex network and wavelet analyses. For the complex network analysis, the correlation coefficient is fixed as 0.8 and the time series is segmented based on the time delay obtained from the autocorrelation function. The signals are classified based on graph features, reflecting the haemodynamic through the mitral valve, using unsupervised principal component analysis and supervised k-nearest neighbour classifier. The appearance of many frequency components in the murmur due to MI is understood from the wavelet analysis. The graphs of NM and MI are found to show two well-defined clusters. When the graph of NM shows a large number of uncorrelated nodes due to the absence of signal in the systolic region that of MI shows interconnected nodes. The improper closing of the mitral valve and the regurgitation of blood in MI results in sound signals in the systolic region, responsible for the increased number of edges compared to NM. The present economical and sensitive graph-based method opens up the plausibility of remote auscultation in primary health centres in the context of the outbreak of pandemic COVID 19.