Heart Sound Multiclass Analysis Based on Raw Data and Convolutional Neural Network

In recent years, automatic diagnosis of the state of health of the heart by employing the phonocardiogram (PCG) has achieved remarkable success. This letter proposes a low-complexity automated solution based on the direct application of a multiclass convolutional neural network to PCG signals for th...

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Bibliographic Details
Published in:IEEE sensors letters 2020-12, Vol.4 (12), p.1-4
Main Authors: Avanzato, Roberta, Beritelli, Francesco
Format: Article
Language:English
Subjects:
Artificial neural networks
Cardiovascular diseases
convolutional neural network (CNN)
Convolutional neural networks
Deep learning
Heart
Heart diseases
heart diseases recognition
Neural networks
phonocardiogram (PCG) signal processing
Phonocardiography
Sensor signals processing
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Summary:In recent years, automatic diagnosis of the state of health of the heart by employing the phonocardiogram (PCG) has achieved remarkable success. This letter proposes a low-complexity automated solution based on the direct application of a multiclass convolutional neural network to PCG signals for the purpose of recognizing and classifying heart disease. PCG signals are fed in to the neural network, bypassing transformations from the time domain to that of frequencies (for example, mel-frequency cepstral coefficients (MFCC), Wavelet, etc.). Applying a recurrence filter in the postprocessing phase, the proposed method allows us to increase the performance in a range from 90 to 100%, with an analysis time of about 34 s. This letter also proposes an analysis of the robustness of the proposed technique to environmental office noise and to real-time PCG sequences.
ISSN:2475-1472
2475-1472
DOI:10.1109/LSENS.2020.3039366