Classification of spontaneous EEG signals in migraine

R. Bellotti, F. De Carlo, M. de Tommaso and M. Lucente

Physica A: Statistical Mechanics and its Applications, 2007, vol. 382, issue 2, 549-556

Abstract: We set up a classification system able to detect patients affected by migraine without aura, through the analysis of their spontaneous EEG patterns. First, the signals are characterized by means of wavelet-based features, than a supervised neural network is used to classify the multichannel data. For the feature extraction, scale-dependent and scale-independent methods are considered with a variety of wavelet functions. Both the approaches provide very high and almost comparable classification performances. A complete separation of the two groups is obtained when the data are plotted in the plane spanned by two suitable neural outputs.

Keywords: Wavelet analysis; Neural networks; Spontaneous EEG (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:382:y:2007:i:2:p:549-556

DOI: 10.1016/j.physa.2007.04.023

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