Classification of spontaneous EEG signals in migraine

✍ Scribed by R. Bellotti; F. De Carlo; M. de Tommaso; M. Lucente

• Book ID: 104080193
• Publisher: Elsevier Science
• Year: 2007
• Tongue: English
• Weight: 189 KB
• Volume: 382
• Category: Article
• ISSN: 0378-4371
• DOI: 10.1016/j.physa.2007.04.023

✦ Synopsis

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.