Morphology analysis of EKG R waves using wavelets with adaptive parameters derived from fuzzy logic

Max Aaron Caldwell; William W. Barrington; Richard R. Miles

doi:10.1117/12.236042

22 March 1996 Morphology analysis of EKG R waves using wavelets with adaptive parameters derived from fuzzy logic

Max Aaron Caldwell, William W. Barrington, Richard R. Miles

Proceedings Volume 2762, Wavelet Applications III; (1996) https://doi.org/10.1117/12.236042
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Max Aaron Caldwell, William W. Barrington, and Richard R. Miles "Morphology analysis of EKG R waves using wavelets with adaptive parameters derived from fuzzy logic", Proc. SPIE 2762, Wavelet Applications III, (22 March 1996); https://doi.org/10.1117/12.236042

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available