ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 125

Extraction of gastric electrical response activity from magnetogastrographic recordings by DCA

Author(s): Carlos Alberto Estombelo-Montesco, Dráulio B. De Araújo, Antônio C. Roque, Eder R. Moraes, Allan K. Barros, Ronald T. Wakai, and Oswaldo Baffa


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 after a meal. MGG recordings were made with a 74-channel first-order gradiometer housed in a shielded room. Seven nonsymptomatic volunteers were measured in the study. Initially a 10 minute recording was dose with the subject in the fasted state. A 250 kcal meal was given to the subject without moving out of the sensor and two epochs of 10 minute each were acquired. 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. The estimation of the signal extracted by DCA might be scaled at the output, to overcome this problem we propose an estimation method based on projection for the scale factor of each signal component extracted. The scaling factor provides two valuable pieces of information. First if we take the high absolute value of , then we have the scaling factor for the output of DCA (estimated source signal). Therefore we can compare their relative amplitudes in the time or frequency domain, and get evidences of ERA signal. Second, we can get the contribution from each channel to the source signal extracted. It allows spatial localization over 37 channels of the estimated source signal. 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). The center of the frequency band and its width varied from subject-to-subject, demonstrating the importance of pre-prandial acquisition as a control. We show that the method is effective in removing interference signals of MGG recordings, and is computationally efficient.

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