Automated Detection of Rheumatic Heart Disease via Unsegmented Heart Sound Analysis

Rheumatic Heart Disease (RHD) remains a significant cause of morbidity and mortality in many parts of the world, particularly in low-resource settings. Early detection and intervention are critical to mitigating its progression and reducing associated complications. This paper presents an automated approach for the detection of RHD utilizing unsegmented heart sound analysis through deep learning techniques. By leveraging Spectro-temporal representations of raw heart sound signals, our proposed method aims to capture subtle yet discriminative patterns indicative of RHD pathology. The deep learning model is trained on a dataset comprising a diverse range of heart sounds, including those from both RHD-positive and RHD-negative individuals. Through rigorous evaluation of unseen data, our approach demonstrates promising performance in accurately distinguishing between RHD and healthy heart sounds. Furthermore, the proposed methodology offers the potential for scalable and cost-effective screening in resource-limited settings, thereby facilitating early identification and management of RHD cases. This work contributes to the advancement of computer-aided diagnostic tools for cardiovascular diseases, with implications for improving healthcare outcomes and reducing the burden of RHD globally.