Year of Publication

1995

Paper Type

Master's Thesis

College

College of Arts and Sciences

Degree Name

Master of Science in Computer and Information Sciences (MS)

Department

Computing

First Advisor

Dr. Layne Wallace

Second Advisor

Dr. Charles Winton

Third Advisor

Dr. Susan Wallace

Abstract

Cardiac auscultation is the primary tool used by cardiologists to diagnose heart problems. Although effective, auscultation is limited by the effectiveness of human hearing. Digital sound technology and the pattern classification ability of neural networks may offer improvements in this area. Digital sound technology is now widely available on personal computers in the form of sound cards. A good deal of research over the last fifteen years has shown that neural networks can excel in diagnostic problem solving. To date, most research involving cardiology and neural networks has focussed on ECG pattern classification. This thesis explores the prospects of recording heart sounds in Wave format and extracting information from the Wave file for use with a backpropagation neural network in order to classify heart patterns.

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