Deep Convolutional Neural Networks for Heart Sound Segmentation

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Renna, F (Author) FCUP; Oliveira, J (Author) Other; Coimbra, M (Author) FCUP

Journal

Title: IEEE Journal of Biomedical and Health Informatics

Vol. 23

Pages: 2435-2445

ISSN: 2168-2194

Publisher: IEEE

Indexing

ISI Web of Knowledge - 38 Citations

Scopus - 56 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11