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Cardiovascular Diseases (CVDs) remain a leading cause of morbidity and mortality
worldwide, necessitating early and accurate detection for effective disease management.
This work employs advanced signal processing techniques in conjunction with machine
learning methodologies to classify normal and particular cardiac conditions—Aortic
Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), and Mitral Valve
Prolapse (MVP)—using phonocardiogram (PCG) signals. Preprocessing involved
denoising using the Discrete Wavelet Transform (DWT) technique with the db8 wavelet
and cA2 component, optimizing noise reduction while retaining valuable features for
further analysis. Feature extraction was performed using Mel-Frequency Cepstral
Coefficients (MFCC) and Mel Power Spectrogram, providing a robust and efficient
representation of heart sounds. Two machine learning models—Deep Neural Network
(DNN) and Convolutional Neural Network (CNN)—were used to assess the extracted
features. With three hidden layers and 80% of the dataset used for training, the DNN model
produced 90%±0.37 accuracy, 89% sensitivity, and 91% specificity. On the other hand, the
CNN model, which consists of two fully connected layers and two convolutional layers
with max pooling, performed by achieving 96%±0.38 accuracy, 95% sensitivity, and 95%
specificity. These results underscore DNN’s enhanced capability in handling complex PCG
data and reducing false negatives. This comprehensive study addresses multiple cardiac
abnormalities, surpassing previous research that often focuses on a single condition or
model. The findings highlight the potential of combining advanced signal processing with
deep learning techniques to improve the timely and accurate identification of cardiac
abnormalities. Future research will explore additional feature extraction methods and larger
datasets to further enhance classification performance. This work significantly contributes
to the field of biomedical engineering, offering a framework to improve patient outcomes
through advanced diagnostic techniques.