Segmentation of heart sound recordings from an electronic stethoscope by a duration dependent Hidden-Markov Model

Digital stethoscopes offer new opportunities for computerized analysis of heart sounds. Segmentation of hearts sounds is a fundamental step in the analyzing process. However segmentation of heart sounds recorded with handheld stethoscopes in clinical environments is often complicated by recording and background noise. A duration-dependent hidden Markov model (DHMM) is proposed for robust segmentation of heart sounds. The DHMM model was developed and tested with heart sounds recorded at bedside with a commercially available handheld stethoscope. In a population of 60 patients, the DHMM identified 739 S1 and S2 sounds out of 744 which corresponded to a 99.3% sensitivity. There were seven incorrectly classified sounds which corresponded to a 99.1% positive predictive value. Our results suggest that DHMM could be a suitable method for segmentation of clinically recorded heart sounds.