Epilepsy detection using Empirical Mode Decomposition and detrended Fluctuation Analysis

In this study, a new method is presented to analyze electroencephalography (EEG) signals by deploying recently proposed adaptive and data driven signal processing method called Empirical Mode Decomposition (EMD). The EMD algorithm represents a signal as a combination of Intrinsic Mode Functions (IMFs) which are extracted from the signal. It is possible to analyze each component of a multi-component signal by using the IMFs. Thus, detrended Fluctuation Analysis (DFA) which is suggested to characterize the auto-correlation properties of non-stationary signals. Frequency and time-frequency domain methods are successfully employed to analyze EEG signals during epileptic seizure. In this study, however, we present a time domain method to analyze and classify EEG signals by investigating the auto-correlation properties of their IMFs extracted by EMD. In the proposed method the IMF features are analyzed by using DFA to determine the epileptic EEG signals.