Epileptic seizure prediction method based on empirical mode decomposition and Kolmogorov complexity

Aiming at the deficiency of scalp electroencephalogram (EEG), a new epileptic seizure prediction method is proposed, where the empirical mode decomposition (EMD) is utilized to remove the artifacts in scalp EEG signals, and the intrinsic mode functions containing the essential information to epileptic seizure prediction is retained. Then, Kolmogorov complexity is employed to reflect the non-linear dynamic characteristics of brains, which reveals that only the Kolmogorov complexity of electrodes near the epileptogenic area decreases significantly before seizures. The algorithm is validated by the clinical data collected from 3 epilepsy patients. The results show that the average prediction period gets 338 seconds, the mean sensitivity reaches to 66.7% and the specificity 19.2%.