title: Why should you model time when you use Markov models for heart sound analysis.
abstract: 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.
authors.forename: Jorge
authors.initials: J
authors.lastname: Oliveira
authors.forename: Theofrastos
authors.initials: T
authors.lastname: Mantadelis
authors.forename: Miguel
authors.initials: M
authors.lastname: Coimbra
mesh_terms: D000293:Adolescent; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D006326:Heart Auscultation / Q000295:instrumentation / Q000379:methods*; D006347:Heart Sounds / Q000502:physiology*; D006801:Humans; D007223:Infant; D008390:Markov Chains; D008955:Models, Cardiovascular; D012815:Signal Processing, Computer-Assisted; D019023:Stethoscopes