Analysis of auditory evoked potential parameters in the presence of radiofrequency fields using a support vector machines method Authors
Received: 22 October 2003 Accepted: 16 April 2004 DOI:
Cite this article as: Maby, E., Le Bouquin Jeannes, R., Liegeok-Chauvel, C. et al. Med. Biol. Eng. Comput. (2004) 42: 562. doi:10.1007/BF02351000 Abstract
The paper presents a study of global system for mobile (GSM) phone radio-frequency 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 −0.36μV for healthy subjects and −0.6OμV for epileptic patients). Healthy subjects showed a NIOO latency decrease (−5.23ms in mean), which could be consistent with mild, localised heating. The auditory cortical activity in humans was modified by GSM phone radio-frequencies, but an effect on brain functionality has not been proven.
Keywords Radiofrequencies Auditory evoked potentials Support vector machines COMOBIO project Health
MBEC online number: 20043906
Basseville, M. (1988): ‘Distances en traitement du signal et reconnaissance de formes’ Publication interne 412
Bear, M. F., Connors, B. W., and Paradiso, M. A. (1999): ‘Neurosciences, la découverte du cerveau’ (Editions Pradel, 1999)
Boutros, N., Nasrallah, H., Leighty, R., Torello, M., Tueting, P.
(1997): ‘Auditory evoked potentials, clinical vs. research applications’,
, pp. 183–195
Burges, R. J.
(1998): ‘A tutorial on support vector machines for pattern recognition’,
Data Mining Knowledge Discovery
, pp. 1–47
Le Bouquin-Jeannès, R.
(2003): ‘K-means clustering method for auditory evoked potentials selection’,
Med. Biol. Eng. Comput.
, pp. 397–402
Guyon, I., Weston, J., Barnhill, S.
(2002): ‘Gene selection for cancer classification using support vector machines’,
, pp. 389–422
Jääskeläinen, I. P., Nätänen, R.
(1996): ‘Effect of acute ethanol on auditory and visual event-related potentials: a review and reinterpretation’,
, pp. 284–291
Jarque, C. M., and Bera, A. K. (1980): ‘Efficient tests for normality homoscedasticity and serial independence of regression residuals’, Econ. Lett., pp. 255–259
Kotchoubey, B., Schneider, D., Uhlmann, C., Schleichert, H.
(1997): ‘Beyond habituation: long-term repetition effects on visual event-related potentials in epileptic patients’,
Electroencephalogr: Clin. Neurophysiol.
, pp. 450–456
(1998): ‘Stationarity index for abrupt changes detection in the time-frequency plane’,
IEEE Signal Process. Lett.
, pp. 43–45
Maby, E., Chaillou, S., Marquis, P., and Le Bouquin Jeannès, R. (2002): ‘Characterization of auditory evoked potentials recorded in radiofrequency fields’. XI European Signal Processing Conf., Toulouse, France
Oppenheim, A. V., and Schafer, R. W. (1975): ‘Digital signal processing’ (Prentice Hall, 1975)
Preece, A. W., Iwi, G., Davies-Smith, A.
(1999): ‘Effect of a 915-MHz simulated mobile phone signal on cognitive function in man’,
Int. J. Radiat. Biol.
, pp. 447–456
Rogers, R. L., Papanicolaou, A. C., Baumann, S. B., Saydjari, C.
Eisenberg, H. M.
(1990): ‘Neuromagnetic evidence of a dynamic excitation pattern generating the N100 auditory response’,
Electroencephalogr: Clin. Neurophysiol.
, pp. 237–240
Rosburg, T., Kreitschmann-Andermahr, I., Nowak, H.
(2000): ‘Habituation of the auditory evoked field component N100m in male patients with schizophrenia,’
J. Psychiatric Res.
, pp. 245–254
Siegel, S. (1956): ‘Non parametric statistics’ (McGraw-Hill Book Company, New York, 1956)
Soininen, H. S., Karhu, J., Partanen, J., Pääkkönen, A., Jousmäki, V., Hänninen, T.
(1995): ‘Habituation of auditory N100 correlates with amygdaloid volumes and frontal functions in age-associated memory impairment’,
, pp. 927–935
Sokolov, E. N. (1977): ‘The detector, the command neuron and plastic convergence’, Zhurnal Vysshei Nervnoi Deiatelnosti Imeni I P Pavlova, 27, pp. 691–698
Thompson, R. F.
Spencer, W. A.
(1966): ‘Habituation: a model phenomenon for the study of neuronal substrates of behavior’,
, pp. 16–43
Vandoolaeghe, E., van Hunsel, F., Nuyten, D.
(1998): ‘Auditory event related potentials in major depression: prolonged P300 latency and increased P200 amplitude’,
J. Affect. Disord.
, pp. 105–113
Vapnik, V. (1998): ‘Statistical learning theory’ (Wiley, 1998)
Verleger, R., Lefèbre, C., Wieschemeyer, R. and Kömpf, D. (1997): ‘Event-related potentials suggest slowing of brain processes in generalized epilepsy and alterations of visual processing in patients with partial seizures’, Data Mining Knowledge Discovery, 5, pp. 205–219