A Novel Blending Hilbert -Kolmogorov Approach for Epileptic Seizures detection

This paper introduces a new seizure detection method based on EEG signals using Hilbert Transform (HT) and Kolmogorov Complexity (KC). The new method is named Blending Hilbert-Kolmogorov (Blending HT-KC) approach. In the Blending HT-KC method, the EEG signal is converted firstly into a complex helical sequence and then into a binary sequence. From the analytic signal, the instantaneous amplitude-based features are obtained and, from the logical signal, the KC features are extracted. The extracted attributes will then fed into the Support Vector Machine Classifier (SVM). The proposed approach is examined for four classification problems to distinguish between Ictal (during seizure) and normal states as well as between ictal and interictal (between seizures) states of epilepsy. The experimental results demonstrate the excellent performance and suitability of the proposed methodology for the detection of seizure activity.