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Identification of physiological systems: a robust method for non-parametric impulse response estimation

D T Westwick, R E Kearney

    Medical & Biological Engineering & Computing
    |March 1, 1997
    PubMed
    Summary
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    This study introduces a robust method for estimating impulse response functions (IRFs) from noisy data, improving accuracy for non-white inputs and providing confidence bounds. The new technique outperforms classical methods in simulations.

    Area of Science:

    • System identification
    • Signal processing
    • Control theory

    Background:

    • Accurate estimation of non-parametric impulse response functions (IRFs) is crucial for analyzing dynamic systems.
    • Existing IRF estimation techniques often struggle with noisy, finite-length data, especially when the input signal is not white.

    Purpose of the Study:

    • To develop a more robust method for identifying non-parametric impulse response functions (IRFs) from noisy data.
    • To introduce techniques for calculating confidence bounds on the estimated IRFs.
    • To demonstrate the method's superiority over classical approaches, particularly for non-white inputs.

    Main Methods:

    • Analysis of non-parametric impulse response function (IRF) identification using matrix perturbation theory.
    • Development of a novel IRF estimation algorithm.

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  • Application of Monte Carlo simulations to evaluate the method's performance and compare it with existing techniques.

    • Main Results:

    • A novel IRF estimation method was developed, showing increased robustness compared to classical techniques, especially for non-white input signals.
    • Methods for computing confidence bounds on IRF estimates were successfully established.
    • Monte Carlo simulations confirmed the superiority of the new method over traditional approaches.

    Conclusions:

    • The proposed method offers a more robust approach to impulse response function (IRF) identification from noisy data.
    • The developed technique and confidence bound calculations are valuable for system identification tasks.
    • The method's effectiveness was demonstrated through an application to identifying dynamic ankle stiffness in humans.