Assisting Heart Valve Diseases Diagnosis via Transformer-Based Classification of Heart Sound Signals

Abstract

Background: In computer-aided medical diagnosis or prognosis, the automatic classification of heart valve diseases based on heart sound signals is of great importance since the heart sound signal contains a wealth of information that can reflect the heart status. Traditional binary classification algorithms (normal and abnormal) currently cannot comprehensively assess the heart valve diseases based on analyzing various heart sounds. The differences between heart sound signals are relatively subtle, but the reflected heart conditions differ significantly. Consequently, from a clinical point of view, it is of utmost importance to assist in the diagnosis of heart valve disease through the multiple classification of heart sound signals. Methods: We utilized a Transformer model for the multi-classification of heart sound signals. It has achieved results from four abnormal heart sound signals and the typical type. Results: According to 5-fold cross-validation strategy as well as 10-fold cross-validation strategy, e.g., in 5-fold cross-validation, the proposed method achieved a highest accuracy of 98.74% and a mean AUC of 0.99. Furthermore, the classification accuracy for Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, Mitral Valve Prolapse, and standard heart sound signals is 98.72%, 98.50%, 98.30%, 98.56%, and 99.61%, respectively. In 10-fold cross-validation, our model obtained the highest accuracy, sensitivity, specificity, precision, and F1 score all at 100%. Conclusion: The results indicate that the framework can precisely classify five classes of heart sound signals. Our method provides an effective tool for the ancillary detection of heart valve diseases in the clinical setting.