Recognition and classification of cardiac murmurs using ANN and segmentation

A diagnostic system based on Artificial Neural Networks (ANN) is implemented as a detector and classifier of heart murmurs. Segmentation and alignment algorithms serve as important pre-processing steps before heart sounds are applied to the ANN structure. The system enables users to create a classifier that can be trained to detect virtually any desired target set of heart sounds. The output of the system is the classification of the sound as either normal or a type of heart murmur. The ultimate goal of this research is to develop a tool that can be used to help physicians in the auscultation of patients and thereby reduce the number of unnecessary echocardiograms- those that are ordered for healthy patients. Testing has been conducted using both simulated and recorded patient heart sounds. Results are described for a system designed to classify heart sounds as normal, aortic stenosis, or aortic regurgitation. The system is able to classify with up to 85 ± 7.4% accuracy and 95 ± 6.8% sensitivity the tested heart sounds. Results are also described for a system designed to classify heart sounds using the consensus result of two sub-systems: (1) normal or aortic stenosis and (2) normal or aortic regurgitation. The consensus system is able to classify the same set of sounds with up to 96.8 ± 2.2% accuracy and 95.9 ± 5.2% sensitivity.