A Subject-Driven Unsupervised Hidden Semi-Markov Model and Gaussian Mixture Model for Heart Sound Segmentation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2019

Autores

Oliveira, J (Autor) Outra; Renna, F (Autor) FCUP; Coimbra, M (Autor) FCUP

Revista

Título: IEEE Journal of Selected Topics in Signal Processing

Vol. 13

Páginas: 323-331

ISSN: 1932-4553

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 7 Citações

Outras Informações

ID Authenticus: P-00Q-HGY

DOI: 10.1109/jstsp.2019.2908723

Abstract (EN): The analysis of heart sounds is a challenging task, due to the quick temporal onset between successive events and the fact that an important fraction of the information carried by phonocardiogram (PCG) signals lies in the inaudible part of the human spectrum. For these reasons, computer-aided analysis of the PCG can dramatically improve the quantity of information recovered from such signals. In this paper, a hidden semi-Markov model (HSMM) is used to automatically segment PCG signals. In the proposed models, the emission probability distributions are approximated via Gaussian mixture model (GMM) priors. The choice of GMM emission probability distributions allow to apply re-estimation routines to automatically adjust the HSMM emission probability distributions to each subject. Building on the proposed method for fine tuning emission distributions, a novel subject-driven unsupervised heart sound segmentation algorithm is proposed and validated over the publicly available PhysioNet dataset. Perhaps surprisingly, the proposed unsupervised method achieved results in line with state-of-the-art supervised approaches, when applied to long heart sounds.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9