Prediction of heart sound using the xLSTM method for hypertrophic cardiomyopathy

Heart disease has emerged as a major global public health concern, driven by poor dietary habits, unhealthy lifestyle choices, and limited health awareness. Accurate diagnosis of cardiac conditions remains challenging for hospitals and clinical institutions. Hypertrophic cardiomyopathy is an autosomal dominant disorder caused by mutations in sarcomere protein genes that affect the contractile function of cardiac muscle. With the growing adoption of digital health systems, large volumes of patient data are now collected and stored, providing opportunities for computational approaches to support clinical decision making. Machine learning methods have become increasingly important for analysing complex and nonlinear patterns in medical data. This study presents an ensemble-based approach for heart sound classification and introduces an Extended Long Short-Term Memory (xLSTM) model for the detection of cardiac abnormalities. The method was evaluated using acoustic features extracted from phonocardiogram recordings. The proposed model achieved 96.93% accuracy, 93.50% sensitivity, and 99.63% specificity, indicating strong performance in distinguishing normal and abnormal heart sounds. In comparison with previously reported techniques, the ensemble strategy and the xLSTM architecture provided improved accuracy. Model performance was assessed using accuracy, precision, recall, and F1 score, confirming the effectiveness of the proposed approach for automated heart sound analysis.