Detection of the first and second heart sound using probabilistic models

In this study, a methodology based on Hidden Markov Models (HMM) as a probabilistic finite state-machine to model systolic and diastolic interval duration is proposed. The detection of the first (S1) and second (S2) heart sound is performed using a network of two HMM's with grammar constraints to parse sequence of systolic and diastolic intervals. Duration modeling was considered in the HMM model architecture selection based on experimental measurements of systolic and diastolic intervals in normal subjects. Feature extraction of heart sound signals was based on time-cepstral features. Results are presented in terms of detection performance compared with QRS peak annotations of the simultaneous ECG recording. The performance of the proposed approach has been evaluated in 80 subjects. The results showed that the system was effective to detect the first and second heart sounds with sensitivity of 95% and a positive predictive value of 97% and thus provides a promising methodology for heart sound analysis.