Extraction of gastric electrical response activity from magnetogastrographic recordings by DCA

  • Authors:

  • C. A. Estombelo-Montesco;D. B. De Araujo;A. C. Roque;E. R. Moraes;A. K. Barros;R. T. Wakai;O. Baffa

  • Affiliations:

  • Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP, Brazil;Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP, Brazil;Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP, Brazil;Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP, Brazil;Department of Electrical Engineering, Federal University of Maranhao, Sao Luis, Maranhao, Brazil;Department of Medical Physics, Medical School, University of Wisconsin, Madison-WI;Department of Physics and Mathematics, FFCLRP, University of Sao Paulo, Ribeirao Preto, SP, Brazil

  • Venue:
  • ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
  • Year:
  • 2007

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Abstract

The detection of the basic electric rhythm (BER), composed of 3 cycles/minute oscillation, can be performed using SQUID sensors. However the electric response activity (ERA), which is generated when the stomach is performing a mechanical activity, was detected mainly by invasive electrical measurements and only recently one report was published dealing with its detection by magnetic measurements. This study was performed with the aim to detect and extract the ERA and ECA noninvasively before and after a meal. After acquire MGG recordings the signals were processed to extract both source components and remove cardiac interference and others interferences by an algorithm based on Dependent Component Analysis (DCA) then autoregressive and wavelet analysis was performed. Therefore, first, we can compare their relative amplitudes in the time or frequency domain, and get evidences of ERA signal. Second, we can get the spatial contribution from each channel to the source signal extracted. Finally, results have shown that there is an increase in the signal power at higher frequencies around (0.6-1.3 Hz) from ERA source component usually associated with the basic electric rhythm (ECA source component). We show that the method is effective in removing interference signals of MGG recordings, and is computationally efficient.