Automated identification of epileptic seizures in EEG signals based on phase space representation and statistical features in the CEEMD domain

Epileptic seizure detection based on visual inspection by expert physicians is burdensome, and subject to error and bias. In this work, we present a novel method for the automated identification of epileptic seizure using a single-channel EEG signal. We utilize the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique to devise an effective feature extraction scheme for physiological signal analysis, and construct the corresponding growth curve. Then, various statistical features are extracted from the growth curve as the feature set, and this is fed to the random forest classifier for completing the detection. The suitability of the extracted features is established through statistical measures and graphical analysis. The proposed method is evaluated for the well-known problem of classifying epileptic seizure and seizure-free signals using a publically available EEG database from the University of Bonn. To assess the performance of the classification method, 10-fold cross-validation is performed. Compared to state-of-the-art algorithms, the numerical results confirm the superior algorithm performance of the proposed scheme in terms of accuracy, sensitivity, specificity, and Cohen's Kappa statistics.