Paper Title
AI Based Detection of Cardiac Murmur Using Chest Auscultation

This paper introduces an emerging application of artificial intelligence (AI) for the early and remote detection of cardiac murmurs. Using a freely available dataset, the study examines the effectiveness of two most commonly used AI techniques, namely the 2D CNN and LSTM models. The study uses the typical models to compare their relative performance. Subsequently, the better of the two models is utilized and integrated in a web application. The web application enables uploading of the auscultation recordings, it then processes the sound and runs the AI model on it. The output is the classification of sound as normal or with murmur. The experiments for relative comparison of the two AI techniques, demonstrate the superiority of the 2D CNN model over LSTM, as evident through enhanced performance metrics accuracy, recall, and precision. The findings underscore the potential of deep learning algorithms, particularly 2D CNN, in effectively detecting the cardiac murmur detection which can further the cause of improved medical diagnosis and patient care. Keywords - Auscultation, Mel-frequency cepstral coefficient (MFCCs), Convolutional neural network (CNN), Long short term memory (LSTM), cardiac murmurs.