Robust Heart Sound Analysis With MFCC and Light Weight Convolutional Neural Network

Objective: Heart sound analysis is essential for cardiovascular disorder classification. Traditional auscultation and rule-based methods require manual feature engineering and clinical expertise. This work proposes a CNN-based model for automated multiclass heart sound classification. Results: Using MFCC features extracted from segmented real-world recordings, the model classifies heart sounds into murmur, extrasystole, extrahls, artifact, and normal. It achieves 98.7% training accuracy and 91% validation accuracy, with strong precision and recall for normal and murmur classes, and a weighted F1-score of 0.91. Conclusions: The results show that the proposed MFCC-CNN framework is robust, generalizable, and suitable for automated auscultation and early cardiac screening.