Convolutional Neural Networks for Heart Sound Segmentation

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

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

Conference proceedings International

Title: 2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)

Pages: 757-761

European Signal Processing Conference (EUSIPCO)

Rome, ITALY, AUG 03-07, 2018

Indexing

ISI Web of Knowledge - 3 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00Q-2RN

DOI: 10.23919/eusipco.2018.8553120

Abstract (EN): In this paper, deep convolutional neural networks are used to segment heart sounds into their main components. The proposed method is based on the adoption of a novel deep convolutional neural network architecture, which is inspired by similar approaches used for image segmentation. A further post-processing step is applied to the output of the proposed neural network, which induces the output state sequence to be consistent with the natural sequence of states within a heart sound signal (S1, systole, S2, diastole). The proposed approach is tested on heart sound signals longer than 5 seconds from the publicly available PhysioNet dataset, and it is shown to outperform current state-of-the-art segmentation methods by achieving an average sensitivity of 93.4% and an average positive predictive value of 94.5% in detecting S1 and S2 sounds.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5