Real Time Cardiac Monitoring and Disease Prediction Using Digital Stethoscope

Heart disease detection has long been a significant area of medical research. Traditionally, physicians rely on acoustic stethoscopes to assess heart function by listening to heartbeats and identifying anomalies. However, the accuracy of such assessments can be limited due to the flat frequency range and low intensity of heart sounds, often accompanied by background noise, making it challenging even for experienced cardiologists to interpret them accurately. In this proposed work, a novel real-time method is introduced for detecting heart conditions by analyzing heart sounds. The approach takes heart sound signals as input and utilizes ECG feature extraction techniques to classify patients as either normal or abnormal. The system integrates an electronic stethoscope with a software-based heartbeat audio classifier. This integrated solution is capable of distinguishing between normal heartbeats and heart murmurs and can also provide real-time predictions regarding the potential type of heart abnormality.