Neural network-based adaptive time-frequency analysis for electrogastrography signals

W. Zhishun, L. Wenhua, H. Zhenya, Y. Dezhi, C. Z. Jiande

Research output: Contribution to journalArticlepeer-review

Abstract

A novel adaptive time-frequency analyzing methodology based on neural network is presented in this paper. Compared with traditional Short Time Fourier Transform (STFT) and Wigner Distribution (WD), the proposed method has the following advantages over them. 1. In order to pursue the variations of the electrogastrography (EGG) signals and extract their time-frequency feature, a modulated basis function is used as the window function which includes such parameters as shifting., scaling and center frequency and so on. Adjusting these parameters, one can change the center, shape and frequency etc., of the window function so that it can optimally match the analyzed signals; 2. Since EGG signals are non-stationary and nonlinear, the time-frequency parameters of them vary non-linearly. Because neural network (NN) is considered as a optimal nonlinear estimator, one can use NN to evolve these parameters mentioned above not only to obtain higher precision, but also has better adaptability; 3. The proposed method has higher resolution than STFT and WD, and the picture of time-frequency energy distribution is clear, i.e., there is no cross-term interference.

Original languageEnglish (US)
Pages (from-to)244-252
Number of pages9
JournalChinese Journal of Biomedical Engineering
Volume16
Issue number3
StatePublished - Sep 1997
Externally publishedYes

Keywords

  • Electrogastrography
  • Neural network
  • Time-frequency analysis
  • Wavelet transform

ASJC Scopus subject areas

  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering

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