Detection of Cardiovascular Abnormalities Using Artificial Intelligence and Heart Sounds

Abstract

Auscultation of the heart is one of the most crucial techniques physicians use to learn about a patient’s heart. Therefore, a lot of effort has been devoted to developing more sophisticated stethoscopes to assist physicians for better diagnosis. Most of this work has been to design stethoscopes to provide clearer signals. This work is an initial effort to include an Artificial Intelligence (AI) system in the stethoscope to perform a preliminary diagnosis of multiple heart conditions. To train the neural network, heart sounds representing 42 different issues are used. Due to the limited number of training data, noise is added to the available heart sounds. This serves the dual purpose of increasing the training data and to partially account for the variation in the heart sounds collected from different patients. These heart sounds are used to extract features such as mean, median, standard deviation, signal entropy, kurtosis, skewness, etc. for neural network training. An optimal neural network architecture is developed to classify these 42 heart conditions with 98% accuracy.