Why should you model time when you use Markov Models for heart sound analysis

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Oliveira, J (Author) Other; Mantadelis, T (Author) FCUP; Coimbra, M (Author) FCUP

Conference proceedings International

Title: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, FL, USA, August 16-20, 2016

Pages: 3449-3452

Indexing

ISI Web of Knowledge - 1 Citation

Other information

Authenticus ID: P-00M-4PE

DOI: 10.1109/EMBC.2016.7591470

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4