This code provides Matlab algorithms to perform HSMM-based heart sound segmentation. Segmentation performance is further improved when apriori information about the expected duration of the states is incorporated into the model, such as in a hidden semi-Markov model (HSMM). While threshold-based segmentation methods have shown modest success, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events.
PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Goldberger A, Amaral L, Glass L, Hausdorff J, Ivanov PC, Mark R, Mietus JE, Moody GB, Peng CK, Stanley HE. Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., Mietus, J.E., Moody, G.B., Peng, C.K. "PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals.
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.
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