Abstract
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.
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