Identification of Anomalous Phonocardiograms based on Universal Probabilistic Modeling

Automatic analysis of phonocardiograms (PCGs) could be a useful tool assisting medical experts in diagnosing heart's functionality. This work presents an algorithm processing PCGs for identifying existing abnormalities. It is based on a standardized feature set free of domain knowledge, the distribution of which is suitably approximated by a universal hidden Markov model capturing its temporal structure. At the same time, an integral part of the model is an adaptation module responsible for incorporating new data as soon as it is available without requiring complete model retraining. Extensive experiments following a standardized protocol show that the proposed algorithm reaches state of the art performance under noisy conditions in a subject/patient independent manner.