  Cardiovascular diseases (CVDs) can be effectively treated when detected
early, reducing mortality rates significantly. Traditionally, phonocardiogram
(PCG) signals have been utilized for detecting cardiovascular disease due to
their cost-effectiveness and simplicity. Nevertheless, various environmental
and physiological noises frequently affect the PCG signals, compromising their
essential distinctive characteristics. The prevalence of this issue in
overcrowded and resource-constrained hospitals can compromise the accuracy of
medical diagnoses. Therefore, this study aims to discover the optimal
transformation method for detecting CVDs using noisy heart sound signals and
propose a noise robust network to improve the CVDs classification
performance.For the identification of the optimal transformation method for
noisy heart sound data mel-frequency cepstral coefficients (MFCCs), short-time
Fourier transform (STFT), constant-Q nonstationary Gabor transform (CQT) and
continuous wavelet transform (CWT) has been used with VGG16. Furthermore, we
propose a novel convolutional recurrent neural network (CRNN) architecture
called noise robust cardio net (NRC-Net), which is a lightweight model to
classify mitral regurgitation, aortic stenosis, mitral stenosis, mitral valve
prolapse, and normal heart sounds using PCG signals contaminated with
respiratory and random noises. An attention block is included to extract
important temporal and spatial features from the noisy corrupted heart
sound.The results of this study indicate that,CWT is the optimal transformation
method for noisy heart sound signals. When evaluated on the GitHub heart sound
dataset, CWT demonstrates an accuracy of 95.69% for VGG16, which is 1.95%
better than the second-best CQT transformation technique. Moreover, our
proposed NRC-Net with CWT obtained an accuracy of 97.4%, which is 1.71% higher
than the VGG16.
