Adaptive spectral analysis of cutaneous electrogastric signals using autoregressive moving average modelling

J. Chen, J. Vandewalle, W. Sansen, G. Vantrappen, J. Janssens

Research output: Contribution to journalArticlepeer-review

Abstract

The recording of the human, gastric myoelectrical activity, by means of cutaneous electrodes is called electrogastrography (EGG). It provides a noninvasive method of studying electrogastric behaviour. The normal frequency of the gastric signal is about 0·05 Hz. However, sudden changes of its frequency have been observed and are generally considered to be related to gastric motility disorders. Thus, spectral analysis, especially online spectral analysis, can serve as a valuable tool for practical purposes. The paper presents a new method of the adaptive spectral analysis of cutaneous electrogastric signals using autoregressive moving average (ARMA) modelling. It is based on an adaptive ARMA filter and provides both time and frequency information of the signal. Its performance is investigated in comparison with the conventional FFT-based periodogram method. Its properties in tracking time-varying instantaneous frequencies are shown. Its applications to the running spectral analysis of cutaneous electrogastric signals are presented. The proposed adaptive ARMA spectral analysis method is easy to implement and is efficient in computations. The results presented in the paper show that this new method provides a better performance and is very useful for the online monitoring of cutaneous electrogastric signals.

Original languageEnglish (US)
Pages (from-to)531-536
Number of pages6
JournalMedical & Biological Engineering & Computing
Volume28
Issue number6
DOIs
StatePublished - Nov 1 1990
Externally publishedYes

Keywords

  • Autoregressive moving average modelling
  • Electrogastrography
  • Gastric motility
  • Gastric slow wave
  • Spectral analysis

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

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