Auscultation is a widely used technique in clinical
activity to diagnose heart diseases. However, heart sounds are
difficult to interpret because a) of events with very short
temporal onset between them (tens of milliseconds) and b)
dominant frequencies that are out of the human audible
spectrum. In this paper, we propose a model to segment heart
sounds using a semi-hidden Markov model instead of a hidden
Markov model. Our model in difference from the state-of-the-
art hidden Markov models takes in account the temporal
constraints that exist in heart cycles.We experimentally confirm
that semi-hidden Markov models are able to recreate the “true”
continuous state sequence more accurately than hidden Markov
models. We achieved a mean error rate per sample of 0.23.