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Adaptive noise removal from biomedical signals using warped polynomials

W Philips1

  • 1Department of Electronics and Information Systems, University of Gent, Belgium. philips@elis.rug.ac.be

IEEE Transactions on Bio-Medical Engineering
|May 1, 1996
PubMed
Summary

The time-warped polynomial filter (TWPF) effectively removes stationary noise from nonstationary biomedical signals. This adaptive filter offers immediate response to signal changes without needing reference signals, proving useful in ECG, EEG, and blood pressure monitoring.

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Area of Science:

  • Biomedical Signal Processing
  • Digital Signal Filtering
  • Adaptive Filtering Techniques

Background:

  • Nonstationary biomedical signals often contain stationary noise, complicating analysis.
  • Existing adaptive noise removal techniques may have limitations in responsiveness and applicability to non-periodical signals.

Purpose of the Study:

  • Introduce the time-warped polynomial filter (TWPF) as a novel interval-adaptive filter.
  • Address the challenge of removing stationary noise from nonstationary biomedical signals.
  • Provide an alternative adaptive filtering method with improved characteristics.

Main Methods:

  • The TWPF fits warped polynomials to signal segments, acting as a time-varying low-pass filter.
  • An iterative adaptation algorithm bypasses complex local bandwidth computation.

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  • The filter is applied to signal segments or fixed-size intervals for noise removal.

    • Main Results:

    • The TWPF demonstrates immediate reaction to signal property changes, independent of noise reduction levels.
    • It effectively removes stationary noise without requiring a reference signal.
    • Experimental results confirm its utility in ECG, respiratory, blood pressure, and EEG signal processing.

    Conclusions:

    • The TWPF is a valuable tool for adaptive noise removal in nonstationary biomedical signals.
    • Its interval-adaptive nature and ability to handle non-periodical signals offer significant advantages.
    • The filter shows practical applicability in diverse biomedical signal restoration tasks.