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2009 Design of Medical Devices Conference Abstracts

Variable Self-Optimizing Cochlear Model for Heart Murmur Detection/Classification OPEN ACCESS

[+] Author and Article Information
W. Ahmad, M. I. Hayee, S. Burns

Department of Electrical and Computer Engineering,  University of Minnesota, DuluthDepartment of Family and Community Health,  University of Minnesota, Medical School DuluthDepartment of Physiology and Pharmacology,  University of Minnesota, Medical School Duluth

G. Nordehn

Department of Electrical and Computer Engineering,  University of Minnesota, DuluthDepartment of Family and Community Health,  University of Minnesota, Medical School DuluthDepartment of Physiology and Pharmacology,  University of Minnesota, Medical School Duluth

J. L. Fitzakerley

Department of Electrical and Computer Engineering,  University of Minnesota, DuluthDepartment of Family and Community Health,  University of Minnesota, Medical School DuluthDepartment of Physiology and Pharmacology,  University of Minnesota, Medical School Duluth

J. Med. Devices 3(2), 027541 (Jul 09, 2009) (1 page) doi:10.1115/1.3147531 History: Published July 09, 2009

Abstract

Accurate detection and classification of heart murmurs by auscultation is suboptimal and not always definitive. The murmur information perceived by the physician brain is the combined effect of both patient's (human) heart and the physician's ear. The information containing the murmur characterization which is retrieved by the human brain resides in the electrical signal coming out of the cochlea. For the very reasons described here, cochlea-like processing has been successfully applied to multiple speech recognition related technologies. This had not, before our prior work, been applied to human heart murmur analysis. Our prior research consisted of three steps: (1) capturing heart sounds, (2) processing the sounds using a cochlea-like filter, and then, (3) classifying each sound as being normal or a murmur using an artificial neural network (ANN). Previously in our research, we used a static cochlea-like filter model in step 2 as described above, which resulted a significant improvement in terms of accuracy of heart murmur classification. Our cochlear filter analysis helped identify information-rich frequency segments in human heart sound. We want to advance the cochlear filter model from a static to a variable frequency selective model with feedback from ANN for better optimization of the heart murmur classification. The heart sounds will be processed in ways more closely replicating the human cochlea than the static cochlear filter. A variable self optimizing cochlear filter will better reproduce the mechanism of the human cochlea in that it will contain a feedback system from ANN to cochlear processing to automatically select the most useful frequencies based upon a threshold mechanism filtering out those frequencies which do not contain significantly useful information about classification of heart murmur. The output of the sounds in the frequency range remaining (variable self-optimizing cochlear filtered sounds) may then be used by the neural network to make a final decision about murmur classification. Our hypothesis is that a variable self-optimizing cochlear filter will significantly improve the accuracy in classification of heart sounds as normal or murmur when compared to a static cochlear filter. Using this approach, we plan to develop an AI based system which will classify heart sounds with a success rate significantly better than the static cochlear filter previously developed.

Copyright © 2009 by American Society of Mechanical Engineers
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