Mathematical Morphology-Based Envelope Detection for Cardiac Disease Classification from Phonocardiogram Signals

Heart disease remains a significant global health concern, demanding a swift and precise diagnosis. Traditional methods such as stethoscope-based auscultation face difficulties in distinguishing complex heart sounds. To adress this issue, we present an innovative method that combines signal processing and deep learning techniques to analyze phonocardiogram (PCG) signals. Our approach involves segmenting PCG signals, extracting envelopes using mathematical morphological operations, and transforming them into image representations. A convolutional neural network (CNN) is employed to classify these images, achieving impressive results with 99.00% training accuracy and 98.00% validation accuracy, along with minimal losses of 0.01% and 0.13%, respectively. During the final testing phase, our model demonstrated an accuracy of 98.00% and a loss value of 0.17%, reinforcing the robustness of our methodology.