Machine Learning based Heart Murmur Detection and Classification.

Cardiovascular diseases rank among the leading causes of mortality worldwide and the early identification of diseases is of paramount importance. This work focuses on developing a novel machine learning-based framework for early detection and classification of heart murmurs by analysing phonocardiogram signals. Our heart murmur detection and classification pipeline encompasses three classification settings. We first develop a set of methods based on transfer learning to determine the existence of heart murmurs and categorize them as present, absent, or unknown. If a murmur is present it will be classified as normal or abnormal based on its clinical outcome by using 1D convolution and audio spectrogram transformers. Finally, we use Wav2Vec encoder with raw audio data and AdaBoost abstain classifier for heart murmur quality identification. Heart murmurs are categorized based on their specific attributes, including murmur pitch, murmur shape, and murmur timing which are important for diagnosis. Using the PhysioNet 2022 dataset for training and validation, we achieve an 81.08% validation accuracy for murmur presence classification and a 68.23% validation accuracy for clinical outcome classification with 60.52% sensitivity and 74.46% specificity. The suggested approaches provide a promising framework for using phonocardiogram signals for the detection, classification, and quality analysis of heart murmurs. This has significant implications for the diagnosis and treatment of cardiovascular diseases.