Adaptive Detection of Cardiac Acoustic Irregularities Using Signal-Driven Intelligence

Cardiovascular disease (CVD) continues to be a leading cause of death globally, claiming approximately 17.9 million lives each year, as reported by the World Health Organization (WHO). This high mortality rate underscores the need for effective early detection and intervention strategies. Heart sound signals, also known as phonocardiograms (PCGs), hold essential information about cardiac health, providing a non-invasive method to assess heart function. Recent advancements in deep learning have enabled the development of models capable of analyzing heart sounds to detect abnormal features, assisting in early diagnosis and disease prevention. However, the challenges in heart sound data, including imbalanced class distributions, complex feature characteristics, and limited differentiation between sounds like systolic and diastolic murmurs, have restricted the effectiveness of traditional deep learning models. This project presents a novel heart sound anomaly detection algorithm based on the Deep Neural Network Model. The DNN ability to capture both local and global features within a signal makes it particularly well-suited for analyzing heart sound data. The proposed algorithm was tested on the PhysioNet/CinC 2016 public dataset, a widely used dataset for heart sound classification. Experimental results demonstrated a high classification accuracy of 99%, with a specificity of 98.5% and a sensitivity of 98.9%. These metrics signify a substantial improvement over existing methods, highlighting the model’s effectiveness in detecting anomalies in heart sounds. The high sensitivity and specificity rates underscore the model's potential to serve as a reliable tool for early screening and diagnosis of cardiovascular diseases