SpectroCardioNet: An Attention-Based Deep Learning Network Using Triple-Spectrograms of PCG Signal for Heart Valve Disease Detection

Abstract

The phonocardiogram (PCG) signal is used for the early detection of cardiovascular diseases (CVDs) as it captures the heart sound characteristics. In this article, a spectral attention-based deep learning network is proposed for the automatic detection of cardiac disease from the spectrograms of PCG signals, namely SpectroCardioNet. From a given PCG audio signal, in view of simultaneously utilizing both time- and frequency-domain information, spectrograms, delta-spectrograms, and double-delta-spectrograms are generated. The extracted triple-spectrogram representation is applied in the proposed network as a three-channel 2-D input, where it passes spectral and sequential feature paths. In the spectral feature path, a spectral attention block (SAB) is designed to emphasize some regions in the spectrograms based on a deep attention network and its output is then processed through the spectral pattern detectors (SpPDs). On the other hand, in order to extract the temporal behavior of the frequency components of the spectrograms, a 1-D convolution-based sequential feature extractor is also proposed. Extensive experimentation is carried out on two standard PCG datasets and very satisfactory performance is achieved in comparison to that obtained by some existing methods.