Deep Convolutional Neural Networks for Heart Sound Segmentation

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

Autores

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

Revista

Título: IEEE Journal of Biomedical and Health Informatics

Vol. 23

Páginas: 2435-2445

ISSN: 2168-2194

Editora: IEEE

Indexação

ISI Web of Knowledge - 38 Citações

Scopus - 56 Citações

Outras Informações

ID Authenticus: P-00R-8JE

DOI: 10.1109/jbhi.2019.2894222

Abstract (EN): This paper studies the use of deep convolutional neural networks to segment heart sounds into their main components. The proposed methods are based on the adoption of a deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. Different temporal modeling schemes are applied to the output of the proposed neural network, which induce the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). In particular, convolutional neural networks are used in conjunction with underlying hidden Markov models and hidden semi-Markov models to infer emission distributions. The proposed approaches are tested on heart sound signals from the publicly available PhysioNet dataset, and they are shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.9 and an average positive predictive value of 94 in detecting S1 and S2 sounds.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11