Abstract
Different kind of methods has been applied to detect the migraine by using flash stimulation. Especially frequency analysis of EEG signal is the most preferred method to detect the migraine by using flash stimulation. Different flash stimulation frequencies at wide frequency range have been used in migraine detection. But the effects of these flash stimulation frequencies and the most effective frequency can be determined by analyzing these frequencies separately. Since each stimulation frequency has been implemented in different time periods, it is necessary to determine the time period to detect magnitude increase in migraine patients. The aim of this study is to determine the most effective flash stimulation frequency and time duration to detect the migraine. In this study, we analyzed the flash stimulation frequencies and time duration separately for detecting migraine. Performance of each flash stimulation frequency has been determined to detect the migraine by analyzing the power spectrums obtained under 2 Hz, 4 Hz and 6 Hz and artificial neural network has been used to determine the which data has a superior performance. Afterwards we analyzed the 2 s, 4 s, 6 s, 8 s and 10 s of flash stimulation periods separately by observing the power spectrums and the results are verified by using artificial neural network. As a result of this study we proposed the 4 Hz of flash stimulation frequency is the most effective frequency and 8 s time period is necessary to detect the migraine at the beta band of EEG’s T5-T3 channel.
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Waters, W. E., and O'Connor, P. J., Prevalence of migraine. J. Neurol. Neurosurg. Psychiatry 38(6):613–616, 1975.
Ozkul, Y., Gurler, B., Bozlar, S., Uckardes, A., and Karadede, S., Flash visual evoked potentials and electroretinograms in migraine. Neuro-Ophthalmology 25(3):143–150, 2001.
Lia, C., Careninni, L., Degioz, C., and Bottachi, E., Computerized EEG analysis in migraine patients. Ital. J. Neurol. Sci. 16:249–254, 1995.
Wenzel, D., Brandl, U., and Herms, D., Visual evoked potentials in juvenile complicated migraine. Electroencephalogr. Clin. Neurophysiol. 53:59, 1982.
Akben, S.B., Subasi, A., Tuncel, D., Analysis of EEG signals under flash stimulation for migraine and epileptic patients. Journal Of Medical Systems DOI 10.1007/s10916-009-9379-1.
De Tommaso, M., Marinazzo, D., Guido, M., Libro, G., Stramaglia, S., Nitti, L., Lattanzi, G., Angelini, L., and Pellicoro, M., Visually evoked phase synchronization changes of alpha rhythm in migraine: correlations with clinical features. Int. J. Psychophysiol. 57(3):203–210, 2005.
Genco, S., de Tommaso, M., Prudenzano, A. M. P., Savarese, M., and Puca, F. M., EEG features in juvenile migraine: topographic analysis of spontaneous and visual evoked brain electrical activity: a comparison with adult migraine. Cephalalgia 14:41–46, 1994.
Spreafico, C., Frigerio, R., Santoro, P., Ferrarese, C., and Agostoni, E., Visual evoked potentials in migraine. Neurol. Sci. 25:288–290, 2004.
Alberti, A., Mazzotta, G., Galletti, F., and Sarchielli, P., Electroencephalographic brain mapping and migraine. J. Headache Pain 5:47–50, 2004.
De Tommaso, M., Stramaglia, S., Schoffelen, J. M., Guido, M., Libro, G., Losito, L., Sciruicchio, V., Sardaro, M., Pellicoro, M., and Puca, F. M., Steady-state visual evoked potentials in the low frequency range in migraine: a study of habituation and variability phenomena. Int. J. Psychophysiol. 49:165–174, 2003.
Bellotti, R., De Carlo, F., de Tommaso, M., and Lucente, M., Classification of spontaneous EEG signals in migraine. Physica A 382:549–556, 2007.
Adeli, H., Zhou, Z., and Dadmehr, N., Analysis of EEG records in an epileptic patient using wavelet transform. J. Neurosci. Methods 123(1):69–87, 2003.
Kannathal, N., Rajendra, A. U., Paul, J., and Ng, E. Y. K., Analysis of EEG signals with and without reflexology using FFT and auto regressive modelling techniques. J. Chin. Clin. Med. 1(1):12–20, 2006.
Proakis, J. G., and Manolakis, D. G., Digital signal processing principles, algorithms, and applications. Prentice-Hall, New Jersey, 1996.
Kay, S. M., and Marple, S. L., Spectrum analysis—A modern perspective. IEEE. 69(11):1380–1419, 1981.
Kay, S. M., Modern spectral estimation: theory and application. Prentice-Hall, New Jersey, 1988.
Stoica, P., and Moses, R., Introduction to spectral analysis, Chapter 3. Prentice-Hall, New Jersey, 1997.
Isaksson, A., Wennberg, A., and Zetterberg, L. H., Computer analysis of EEG signals with 4 parametric models. Proc. IEEE 69(4):451–461, 1981.
Akaike, H., A new look at the statistical model identification. IEEE Trans. Autom. Control AC. 19:716–723, 1974.
Fausett, L., Fundamentals of neural networks architectures, algorithms, and applications. Prentice Hall, Englewood Cliffs, NJ, 1994.
Miller, A. S., Blott, B. H., and Hames, T. K., Review of neural network applications in medical imaging and signal processing. Med. Biol. Eng. Comput. 30:449–464, 1992.
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Akben, S.B., Subasi, A. & Tuncel, D. Analysis of Repetitive Flash Stimulation Frequencies and Record Periods to Detect Migraine Using Artificial Neural Network. J Med Syst 36, 925–931 (2012). https://doi.org/10.1007/s10916-010-9556-2
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DOI: https://doi.org/10.1007/s10916-010-9556-2