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Proceedings Paper

Morphology analysis of EKG R waves using wavelets with adaptive parameters derived from fuzzy logic
Author(s): Max Aaron Caldwell; William W. Barrington; Richard R. Miles
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Paper Abstract

Understanding of the EKG components P, QRS (R wave), and T is essential in recognizing cardiac disorders and arrhythmias. An estimation method is presented that models the R wave component of the EKG by adaptively computing wavelet parameters using fuzzy logic. The parameters are adaptively adjusted to minimize the difference between the original EKG waveform and the wavelet. The R wave estimate is derived from minimizing the combination of mean squared error (MSE), amplitude difference, spread difference, and shift difference. We show that the MSE in both non-noise and additive noise environment is less using an adaptive wavelet than a static wavelet. Research to date has focused on the R wave component of the EKG signal. Extensions of this method to model P and T waves are discussed.

Paper Details

Date Published: 22 March 1996
PDF: 9 pages
Proc. SPIE 2762, Wavelet Applications III, (22 March 1996); doi: 10.1117/12.236042
Show Author Affiliations
Max Aaron Caldwell, Univ. of Nebraska Medical Ctr. (United States)
William W. Barrington, Univ. of Nebraska Medical Ctr. (United States)
Richard R. Miles, Univ. of Nebraska Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 2762:
Wavelet Applications III
Harold H. Szu, Editor(s)

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