Analysis of epileptic EEG signals by means of empirical mode decomposition and time-varying two-sided autoregressive modelling

Abstract

The presentation concerns the analysis of EEG recordings for epileptic seizure detection. The EEG signal is decomposed adaptively into oscillating components called implicit mode functions (IMFs) using the empirical mode decomposition (EMD) algorithm and then a time-frequency analysis is carried out on the first two components using a parametric time-frequency distribution based on two-sided autoregressive modeling. The local frequency of the IMF extracted at some iteration is lower than that of the IMF extracted at the previous iteration which enables to analyze the signal at different scales. The relative variation of the instantaneous frequency of the IMFs estimated using two-sided autoregressive model-based time-frequency distribution is used as a feature for automatic seizure detection in the EEG recordings. The effectiveness of the proposed method is demonstrated on EEG signals recorded from 18 patients suffering from different kinds of epileptic seizures as well as on normal EEG data recorded from control population. © 2009 Springer Berlin Heidelberg.