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B Gourevitch

Publications and source records attributed to B Gourevitch.

2 recordsLinked to original sources

Analysis of auditory evoked potential parameters in the presence of radiofrequency fields using a support vector machines method.

The paper presents a study of global system for mobile (GSM) phone radiofrequency effects on human cerebral activity. The work was based on the study of auditory evoked potentials (AEPs) recorded from healthy humans and epileptic patients. The protocol allowed the comparison of AEPs recorded with or without exposure to electrical fields. Ten variables measured from AEPs were employed in the design of a supervised support vector machines classifier. The classification performance measured the classifier's ability to discriminate features performed with or without radiofrequency exposure. Most significant features were chosen by a backward sequential selection that ranked the variables according to their pertinence for the discrimination. Finally, the most discriminating features were analysed statistically by a Wilcoxon signed rank test. For both populations, the N100 amplitudes were reduced under the influence of GSM radiofrequency (mean attenuation of -0.36 microV for healthy subjects and -0.60 microV for epileptic patients). Healthy subjects showed a N100 latency decrease (-5.23 ms in mean), which could be consistent with mild, localised heating. The auditory cortical activity in humans was modified by GSM phone radiofrequencies, but an effect on brain functionality has not been proven.

Cell Phone↗

K-means clustering method for auditory evoked potentials selection.

Surface auditory evoked potentials are generally recorded using a headset of 32, 64 or 128 electrodes, but the quality of the responses is quite heterogeneous on the scalp surface. In some contexts, such as the analysis of auditory evoked potentials recorded in radio-frequency fields, the signal quality is essential, and it appears pertinent to consider only a limited number of electrodes. Therefore, before analysing signals influenced by radio-frequency fields, it is necessary to consider the preliminary step of selecting the channels where auditory activity is strong. This step is often realised by human visual selection and can take a considerable time. In this paper, a simple k-means clustering method is proposed, to select automatically the important channels, and the results are compared with traditional methods of selection. The method detected channels that showed a concordance rate of 86.5% with the visual selection (performed by five individuals) and gave the same final selection (only two extra electrodes in the automatic case). Moreover, the time needed for this automatic selection was 100 times less than that for the visual selection, and also human variability was avoided.

Algorithms↗