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

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

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

Conference proceedings International

Title: Computing in Cardiology

Pages: 813-816

43rd Computing in Cardiology Conference, CinC 2016

11 September 2016 through 14 September 2016

Indexing

Scopus - 129 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 3