Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena

A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/ deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set.

Authors:

• Mohammad Zavid Parvez
• Manoranjan Paul

Keywords:

• Epilepsy
• Seizure
• Phase Correlation
• Fluctuation
• Deviation.

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