Heart sound detection in respiratory sound using Hidden Markov Model

In this work, we have investigated the heart sound (HS) detection performance of Hidden Markov Model (HMM) in respiratory sound. Respiratory sound is composed of heart sound and lung sound, and the main frequency components of these two sounds overlap with each other. To detect the locations of heart sound segments in such adverse condition accurately, the proposed method employs following steps. First, the Shannon entropy feature is extracted for robust representation of respiratory signal for different flow rates. Second, the probabilistic models are constructed by training HMM. Finally, the location of heart sound segments are efficiently estimated by the Viterbi decoding algorithm. The experimental results showed that the proposed heart sound detection method outperforms the three well-known heart sound detection methods in the literature. The average false negative rate (FNR) values for the proposed method are 5.4 ± 2.4 and 6.3 ± 1.3 for both low and medium respiratory flow rate, respectively, which are significantly lower than that of the compared methods in the literature.