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Waveform estimation from noisy signals with variable signal delay using bispectrum averaging

M Nakamura1

  • 1Biomedical Engineering Unit, Tokai University School of Medicine, Ischara, Japan.

IEEE Transactions on Bio-Medical Engineering
|February 1, 1993
PubMed
Summary
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This study introduces a bispectrum averaging technique to recover noisy signal waveforms without time alignment. It compares recursive and least squares methods for Fourier analysis, demonstrating effectiveness in evoked potential studies.

Area of Science:

  • Signal Processing
  • Biomedical Engineering
  • Data Analysis

Background:

  • Recovering signal waveforms from noisy data with variable delays is challenging.
  • Existing methods often require explicit time alignment or initial signal estimates.
  • Bispectrum averaging offers a potential solution for these limitations.

Purpose of the Study:

  • To present a novel bispectrum averaging technique for signal waveform recovery.
  • To compare the performance of recursive and least squares algorithms for Fourier analysis of averaged bispectra.
  • To evaluate the technique's effectiveness using simulated and physiological data, including evoked potential studies.

Main Methods:

  • Utilized bispectrum averaging to process noisy signals with variable delays.

Related Experiment Videos

  • Developed and compared two algorithms: recursive method and least squares method for Fourier amplitude and phase recovery.
  • Numerically investigated algorithm performance with computer-generated data and a physiological signal.

    • Main Results:

    • The bispectrum averaging technique successfully recovers signal waveforms without explicit time alignment or initial signal estimates.
    • Both recursive and least squares methods were investigated, with their advantages and disadvantages discussed.
    • Experimental results from evoked potential studies demonstrated the technique's effectiveness.

    Conclusions:

    • The described bispectrum averaging technique is effective for recovering signal waveforms from noisy, time-delayed data.
    • The comparison of recursive and least squares algorithms provides insights into their application in Fourier analysis.
    • The technique shows promise for various applications, particularly in analyzing physiological signals like evoked potentials.