PubMed · 5073003
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J Pietruski, M Konopski. 1972. [PeKa electrogustometer designed by us].. https://pubmed.ncbi.nlm.nih.gov/5073003/
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Spontaneous depolarisation and repolarisation due to ionic exchange are the main properties of smooth muscle cells in the human stomach. This change in the distribution of electrical charge results in the creation of an electric field. The field manifests itself as a potential difference (biovoltage), recorded both in vitro and in vivo and known as gastric electrical activity (GEA). The aim of the paper is to describe a computer model of this electric phenomenon, considering all anatomical and electrophysiological particularities of the stomach, and to simulate real in vivo experiments with a computer. In the proposed model, the depolarised smooth muscle cells are represented as organised electrical dipoles distributed with known density in an annular band that moves distally with increasing velocity. Computer simulations of in vivo experiments using this model not only give the waveform, duration, amplitude and frequency of GEA, but they also represent the phase lag between different channels, the difference in propagation velocity along greater and lesser curvatures, and the electric coupling between different parts of the stomach. The effects of changed electrode configuration, surface area and distance from the stomach are described. Mathematical modelling is done in spherical co-ordinates, and the simulations are performed in a specially designed user-friendly IBM PC environment. Some of the unsolved problems in cutaneous electrogastrography are also discussed.
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Many clinical and research applications rely on detecting evoked potential (EP) signal or EP differences between conditions. Statistical methods for objective signal detection should be sensitive to the presence of signal, but must provide the user strict control on tolerated false alarm rate. The respective sensitivities of 6 signal detection methods were compared through several Monte Carlo simulations involving 2 autocorrelation structures, 5% and 1% significance levels, 8, 10 or 12 replications per study, and increasing signal to noise ratio. The signal detection methods compared were: (1) the Record Orthogonality Test by Permutations (ROT-p), a variant of the Residual Orthogonality Test (Achim et al., 1988), that provides an unbiased estimate of the energy of the signal present in the averaged data, (2) the Tsum2 permutation test of Karniski et al. (1994), (3) a Principal Component Analysis method (PC1) consisting of a t test on the weights of the first principal component, (4) multiple t tests on amplitudes with empirical adjustment for global false alarm rate, and (5-6) the test of Guthrie and Buchwald (1991) on length of consecutive t tests significant at P < 0.05 or 0.01 per-test. The first 3 methods did not exceed their nominal false alarm rate and clearly outperformed the last 3, with the ROT-p method being significantly more sensitive than all others under almost all conditions.