Automated System for Epileptic EEG Detection Using Iterative Filtering

The nonstationary characteristics present in electroencephalogram (EEG) signal require a crucial analysis that can reveal a method for diagnosis of neurological abnormalities, especially epilepsy. This article presents a new technique for automated classification of epileptic EEG signals based on iterative filtering (IF) of EEG signals. The superiority of IF over empirical mode decomposition for the classification of seizure EEG signals is presented. In this article, EEG epochs are decomposed into their intrinsic mode functions (IMFs) using IF. Amplitude envelope (AE) function is extracted from these modes, using the discrete separation energy algorithm. The features are extracted from these IMFs and AE functions. The feature set includes K-nearest neighbor entropy estimator, log energy entropy, Shannon entropy, and Poincar $\acute{\text{e}}$ plot parameters. These features are tested for their discriminative strength, on the basis of their $p$ -values, for classification of EEG signals into seizure, seizure-free, and normal classes. This proposed methodology has obtained a high classification accuracy using random forest classifier and takes far less time, which can be suitable for real-time implementation.