Heart murmur detection using Ensemble Empirical Mode Decomposition and derivations of the Mel-Frequency Cepstral Coefficients on 4-area phonocardiographic signals

This paper presents an automatic detection system for the classification of phonocardiographic (PCG) signals using 4 standard auscultation areas (one of each cardiac valve) for heart murmur diagnosis. The database of 4-area PCG records belongs to the National University of Colombia. A set of 50 individuals were labeled as normal, while 98 were labeled as exhibiting cardiac murmurs, caused by valve disorders. With the help of medical experts, 400 representative beats were chosen, 200 normal and 200 with evidence of cardiac murmur from 4 different areas of auscultation. First, the PCG signals were preprocessed; next, four different derivations of Mel Frequency Cepstral Coefficients (MFCC) were extracted. Additionally, statistical moments of Hilbert Huang Transform (HHT) were estimated using different combinations of the signal components by means of Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD) and Complete EEMD with Adaptative Noise (CEEMDAN), independently, where the computational complexity were compared. Finally, stochastic analysis of the feature space was carried out by an ergodic-HMM and the global classification result was around 98% with acceptable sensitivity and specificity scores, using a 30-fold cross-validation procedure (70/30 split).