Feature Extraction and Classification of PCG Signal

Analysis of Phonocardiogram (PCG) data is crucial in the diagnosis of cardiovascular conditions. This research introduces an innovative method for automatically categorizing heart sounds into five distinct groups: murmurs, artifacts, extrasystole, extrahls, and normal sounds. Advanced machine learning techniques are used to extract Mel-Frequency Cepstral Coefficients (MFCCs) from PCG signals as discerning features. Augmentation methods are employed to increase the training dataset, thereby enhancing the model's generalization capability. The classification is carried out using the K- Nearest Neighbors (KNN) algorithm, which achieves an impressive 91% accuracy across the specified categories. The developed framework showcases the effectiveness of machine learning in automating heart sound analysis, leading to improved diagnostic precision and efficiency. The reproducible nature of the provided code enables wider adoption and facilitates further research in this domain. This work contributes to advancing cardiac diagnostics, offering valuable insights for both clinical practice and research in cardiovascular health.