Paper Title
AI Based Detection of Cardiac Murmur Using Chest Auscultation
Abstract
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.