Classifying Heart Sound Recordings using Deep Convolutional Neural Networks and Mel-Frequency Cepstral Coefficients

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Rubin, J (Autor) Outra; Rui Abreu (Autor) FEUP; Ganguli, A (Autor) Outra; Nelaturi, S (Autor) Outra; Matei, I (Autor) Outra; Sricharan, K (Autor) Outra

Ata de Conferência Internacional

Título: Computing in Cardiology

Páginas: 813-816

43rd Computing in Cardiology Conference, CinC 2016

11 September 2016 through 14 September 2016

Indexação

Scopus - 129 Citações

Outras Informações

ID Authenticus: P-00P-KBF

DOI: 10.22489/cinc.2016.236-175

Abstract (EN): We describe the development of an algorithm for the automatic classification of heart sound phonocardiogram waveforms as normal, abnormal or uncertain. Our approach consists of three major components: 1) Heart sound segmentation, 2) Transformation ofone-dimensional waveforms into two-dimensional time-frequency heat map representations using Mel-frequency cepstral coefficients and 3) Classification of MFCC heat maps using deep convolutional neural networks. We applied the above approach to produce submissions for the 2016 PhysioNet Computing in Cardiology Challenge. We present results from the challenge, as well as describe in detail the resulting neural network architecture produced and design decisions made.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 3