2018
Authors
Renna, F; Oliveira, J; Coimbra, MT;
Publication
2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
Abstract
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.
2018
Authors
Filipe, S; Coelho, AS; Barbosa, B; Santos, CA;
Publication
EDULEARN18: 10TH INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING TECHNOLOGIES
Abstract
2018
Authors
Santos, CA; Barbosa, B; Filipe, S;
Publication
EDULEARN18: 10TH INTERNATIONAL CONFERENCE ON EDUCATION AND NEW LEARNING TECHNOLOGIES
Abstract
2018
Authors
Filipe, S; Santos, CA; Barbosa, B;
Publication
CBU INTERNATIONAL CONFERENCE PROCEEDINGS 2018: INNOVATIONS IN SCIENCE AND EDUCATION
Abstract
2018
Authors
Barbosa, B; Silva, D; Santos, CA; Filipe, S;
Publication
CBU INTERNATIONAL CONFERENCE PROCEEDINGS 2018: INNOVATIONS IN SCIENCE AND EDUCATION
Abstract
2018
Authors
Barbosa, B; Remondes, J; Teixeira, S;
Publication
INTERNATIONAL JOURNAL OF MARKETING COMMUNICATION AND NEW MEDIA
Abstract
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