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

Abstract:

The Karhunen-Loeve (KL) transform is a tool to analyze 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 same performance than RLS with lower calculations and without the numerical instability problem, making it the most suitable for the KL estimation.

Año de publicación:

1996

Keywords:

    Fuente:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

    • Algoritmo
    • Procesamiento de señales

    Áreas temáticas:

    • Cirugía y especialidades médicas afines

    Contribuidores:

    García J.M.García J.M.
    Olmos S.Olmos S.
    Pablo LagunaPablo Laguna