Using Soft Attention Mechanisms to Classify Heart Sounds

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Oliveira, J (Autor) Outra; Nogueira, M (Autor) Outra; Ramos, C (Autor) Outra; Renna, F (Autor) FCUP; Ferreira, C (Autor) Outra; Coimbra, M (Autor) FCUP

Ata de Conferência Internacional

Título: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Páginas: 6669-6672

41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019

23 July 2019 through 27 July 2019

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00R-4P2

DOI: 10.1109/embc.2019.8856748

Abstract (EN): Recently, soft attention mechanisms have been successfully used in a wide variety of applications such as the generation of image captions, text translation, etc. This mechanism attempts to mimic the visual cortex of a human brain by not analyzing all the objects in a scene equally, but by looking for clues (or salient features) which might give a more compact representation of the environment. In doing so, the human brain can process information more quickly and without overloading. Having learned this lesson, in this paper, we try to make a bridge from the visual to the audio scene classification problem, namely the classification of heart sound signals. To do so, a novel approach merging soft attention mechanisms and recurrent neural nets is proposed. Using the proposed methodology, the algorithm can successfully learn automatically significant audio segments when detecting and classifying abnormal heart sound signals, both improving these classification results and somehow creating a simple justification for them.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4