Detection and Classification of Cardiac Murmurs using Segmentation Techniques and Artificial Neural Networks

Spencer L. Strunic, Fernando Rios-Gutierrez, Rocio Alba-Flores, Glenn Nordehn, Stanley Burns

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 for a group of 72 simulated heart sounds. The accuracy rate of the ANN system for simulated sounds is compared to the accuracy rate of a group of medical students who were asked to classify heart sounds from the same group of sounds classified by the ANN system.

Subjects: 1.5 Diagnosis; 14. Neural Networks

Submitted: Feb 12, 2007

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