Online adaptive seizure prediction algorithm for scalp EEG

Epilepsy is a brain disorder, which affects around 1% of world population. The life of epilepsy patients can be improved by predicting seizures before its occurrence. It has been observed that EEG signals during the pre-seizure state are less chaotic compared to their behavior at normal state. Therefore, chaoticity measure can be used to develop seizure predictor. In this paper, we propose seizure prediction algorithm based on Largest Lyapunov Exponent (LLE) to measure the chaoticity of scalp EEG signals. The proposed algorithm makes use of LLE to define two baselines; one for the normal state and the other for the pre-state. The distance between the two baselines and the LLEs of an Electroencephalography (EEG) signal of unknown state is computed for signal classification. The two baselines are updated through a simple mechanism. The performance of proposed algorithm has been evaluated using MIT database.

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