Conference proceedings

Optimal local polynomial regression of noisy time-varying signals

Sreenivasa Murthy A, Sreenivas T ...see all

European Signal Processing Conference (2008)

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We address the problem of local-polynomial modeling of smooth time-varying
signals with unknown functional form, in the presence of additive noise.
The problem formulation is in the time domain and the
polynomial coefficients are estimated in the pointwise minimum mean square
error (PMMSE) sense. The choice of the window length for local modeling
introduces a bias-variance tradeoff, which we solve optimally by using the
intersection-of-confidence-intervals (ICI) technique. The combination
of the local polynomial model and the ICI technique gives rise to an
adaptive signal model equipped with a time-varying PMMSE-optimal window
length whose performance is superior to that obtained by using a fixed
window length. We also evaluate the sensitivity of the ICI technique with
respect to the confidence interval width. Simulation results on
electrocardiogram (ECG) signals show that at 0dB signal-to-noise ratio
(SNR), one can achieve about 12dB improvement in SNR. Monte-Carlo
performance analysis shows that the performance is comparable to the basic
wavelet techniques. For 0 dB SNR, the adaptive window technique yields
about 2-3dB higher SNR than wavelet regression techniques and for SNRs
greater than 12dB, the wavelet techniques yield about 2dB higher SNR.

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  • PUI: 365227601
  • ISSN: 22195491
  • SCOPUS: 2-s2.0-84863746759
  • SGR: 84863746759


  • A. Sreenivasa Murthy

  • T. V. Sreenivas

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