Detection of Heart Murmurs Using Wavelet Analysis and Artificial Neural Networks

[+] Author and Article Information
Nicholas Andrisevic, Khaled Ejaz, Fernando Rios-Gutierrez, Stanley Burns

Department of Electrical and Computer Engineering, University of Minnesota, Duluth, MN 55812

Rocio Alba-Flores

Department of Electrical and Computer Engineering, University of Minnesota, Duluth, MN 55812nandrise@d.umn.edu

Glenn Nordehn

Department of Family Medicine, University of Minnesota School of Medicine Duluth, Duluth, MN 55812gnordeh1@d.umn.edu

J Biomech Eng 127(6), 899-904 (Jul 08, 2005) (6 pages) doi:10.1115/1.2049327 History: Received March 30, 2005; Revised July 08, 2005

This paper presents the algorithm and technical aspects of an intelligent diagnostic system for the detection of heart murmurs. The purpose of this research is to address the lack of effectively accurate cardiac auscultation present at the primary care physician office by development of an algorithm capable of operating within the hectic environment of the primary care office. The proposed algorithm consists of three main stages. First; denoising of input data (digital recordings of heart sounds), via Wavelet Packet Analysis. Second; input vector preparation through the use of Principal Component Analysis and block processing. Third; classification of the heart sound using an Artificial Neural Network. Initial testing revealed the intelligent diagnostic system can differentiate between normal healthy heart sounds and abnormal heart sounds (e.g., murmurs), with a specificity of 70.5% and a sensitivity of 64.7%.

Copyright © 2005 by American Society of Mechanical Engineers
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Figure 1

Normal heart sound spectrogram

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Figure 2

Representative heart sound patterns

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Figure 3

The x and y axes correspond to time and frequency, respectively. The color represents the amplitude, or intensity, at that given time/frequency point.

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Figure 4

Clockwise from upper-left: Normal heart, pulmonary stenosis, aortic stenosis, mitral stenosis

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Figure 5

(a) Division of the image into blocks; (b) Result of block processing

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Figure 6

The four images from Fig. 4, after block processing

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Figure 7

Input vectors after PCA

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Figure 8

Block diagram of the neural network, adapted from 34

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Figure 9

Example training session report of the neural network



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