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.