Automatic segmentation of heart sound signals using hidden markov models

The monitoring of respiration rates using impedance plethysmography is often confused by cardiac activity. This paper proposes using the phonocardiogram as an alternative, since the process of respiration affects heart sounds. As part of this research, a technique is developed to segment heart sounds into its component segments, using hidden Markov models. The heart sounds data is preprocessed into feature vectors, where the feature vectors are comprised of the average Shannon energy of the heart sound signal, the delta Shannon energy, and the delta-delta Shannon energy. The performance of the segmentation system is validated using eight-fold cross-validation