Performance of RLS and LMS algorithms in KL estimation of ischemic ECG records

Author

García, José ; Olmos, Salvador ; Laguna, Pablo

Author_Institution

Centro Politecnico Superior, Zaragoza Univ., Spain

Volume

4

fYear

1996

fDate

31 Oct-3 Nov 1996

Firstpage

1357

Abstract

The Karhunen-Loeve (KL) transform is a tool to analyse the repolarization period in the ECG. Adaptive algorithms improve the KL series estimation. The recursive least squares (RLS) and least mean squares (LMS) algorithms are studied when applied to estimate the KL coefficients of the ST-T complex in the ECG signal. The performance of RLS and LMS algorithms are compared both in improvement of signal-to-noise ratio (SNR) and in convergence rate. It is presented a specific initialization for the LMS algorithm that obtains the same performance than RLS with lower calculations and without the numerical instability problem, making it the most suitable for the KL estimation

Keywords

adaptive estimation; adaptive filters; convergence of numerical methods; electrocardiography; least mean squares methods; least squares approximations; medical signal processing; recursive estimation; transforms; Karhunen-Loeve transform; LMS algorithms; RLS algorithms; SNR improvement; ST-T complex; adaptive algorithms; algorithm performance; convergence rate; ischemic ECG records; linear transform; repolarization period; Adaptive algorithm; Adaptive filters; Convergence; Electrocardiography; Ischemic pain; Least squares approximation; Nonlinear filters; Resonance light scattering; Signal to noise ratio; Transversal filters;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE

Conference_Location

Amsterdam

Print_ISBN

0-7803-3811-1

Type

conf

DOI

10.1109/IEMBS.1996.647452

Filename

647452