Identification of Coronary Artery Disease using Cross Power Spectral Density

Heart sounds provide important information about cardiac status either through auscultation or by processing of electronic recordings of heart sounds, termed as phonocardiogram (PCG). The current study analyzes the relation between PCG signal from four auscultation sites for diagnosis of coronary artery disease (CAD). For this purpose, data collected from four locations on chest is analysed using cross power spectral density (CPSD). The features from spectrum are derived using distribution of power and their moments in subbands. ReliefF algorithm is employed for selecting features to be used in classification framework. Performance is evaluated using five-fold cross-validation in a support vector machine (SVM) classifier. Experimental results show that multichannel analysis performs better than existing features, as well as for same CPSD based features derived from single channel power spectrum. Different subband width were experimentally analysed to find an optimal width for feature extraction. The study shows the potential of using connectivity between PCG signals from multiple sites for diagnosing CAD related abnormality.