Abnormal Heart Sound Detection Using Common Spatial Patterns and Random Forests

Early and accurate diagnosis of heart conditions is pivotal for effective treatment. Phonocardiography (PCG) has become a standard diagnostic tool for evaluating and detecting cardiac abnormalities. While traditional cardiac auscultation remains widely used, its accuracy is highly dependent on the clinician’s experience and auditory skills. Consequently, there is a growing need for automated, objective methods of heart sound analysis. This study explores the efficacy of the Common Spatial Patterns (CSP) feature extraction algorithm paired with the Random Forest (RF) classifier to distinguish between normal and pathological heart sounds. The signal is denoised, transformed, and segmented into fixed-length segments. CSP is applied to extract discriminative features (a set of Spatial Patterns), which are then fed into the classifier for cardiac diagnosis. The proposed method was evaluated using PhysioNet/CinC Challenge 2016 and Yaseen2018 (Heart Sound Murmur) datasets. On the testing set of the PhysioNet dataset, the RF classifier achieved 100% precision, recall, accuracy, F1 score, and AUC. Similarly, on the testing set of the Yaseen2018 dataset, it achieved 96.30% precision, 1.00 recall, 98.08% accuracy, 98.11% F1 score, and 99.41% AUC.