A computer-aided MFCC-based HMM system for automatic auscultation.

Auscultation, the act of listening to the sounds of internal organs, is a valuable medical diagnostic tool. Auscultation methods provide the information about a vast variety of internal body sounds originated by various organs such as heart, lungs, bowel, vascular disorders, etc. In this study, a cardiac sound registration system has been designed incorporating functions such as heart signals segmentation, classification and characterization for automated identification and ease of interpretation by the users. Considering a synergy with the domain of speech analysis, the authors introduced Mel-frequency cepstral coefficient (MFCC) to extract representative features and develop hidden Markov model (HMM) for signal classification. This system was applied to 1381 data sets of real and simulated, normal and abnormal domains. Classification rates for normal and abnormal heart sounds were found to be 95.7% for continuous murmurs, 96.25% for systolic murmurs and 90% for diastolic murmurs by a probabilistic comparison approach. This implies a high potential for the system as a diagnostic aid for primary health-care sectors.