Chaos feature study in fractional Fourier domain for preictal prediction of epileptic seizure

Epileptic seizure prediction is limited by the unstable performance of suboptimal models. Studies of new methods for reliable preictal prediction have significant impact on the control, early care and online treatment of epileptic seizure. The traditional chaos measure does not effectively identify multiple states of epileptic electroencephalogram (EEG). A novel method was adopted to capture subtle chaotic dynamics for epileptic signals in fractional Fourier transform domain. Algorithm of the largest Lyapunov exponent was modified to adapt the transformed series by using an energy measure to determine appropriate fractional order. The performance of our proposed method was evaluated with an automatic model of preictal prediction using artificial neural networks as classifier. The results showed that the new model yielded higher accuracy in identifying the preictal state compared to the original largest Lyapunov exponent. Experimental results with noisy scalp epileptic EEGs also demonstrated the potential and robustness of our approach to discriminate preictal from interictal and ictal states, and it provided a novel methodology for reliable preictal prediction of epileptic seizure.