Cardiac disorder classification by heart sound signals using murmur likelihood and hidden markov model state likelihood

This study proposes a new algorithm for cardiac disorder classification by heart sound signals. The algorithm consists of three steps: segmentation, likelihood computation and classification. In the segmentation step, the authors convert heart sound signals into mel-frequency cepstral coefficient features and then partition input signals into S1/S2 intervals by using a hidden Markov model (HMM). In the likelihood computation step, using only a period of heart sound signals, the authors compute the HMM `state` likelihood and murmur likelihood. The `state` likelihood is computed for each state of HMM-based cardiac disorder models, and the murmur likelihood is obtained by probabilistically modelling the energies of band-pass filtered signals for the heart pulse and murmur classes. In the classification step, the authors decided the final cardiac disorder by combining the state likelihood and the murmur likelihood by using a support vector machine. In computer experiments, the authors show that the proposed algorithm greatly improve classification accuracy by effectively reducing the classification errors for the cardiac disorder categories where the temporal murmur position plays an important role in detecting disorders.