Discrimination between Ictal and Seizure‐Free EEG Signals Using Empirical Mode Decomposition

Ram Bilas Pachori

doi:10.1155/2008/293056

Abstract

A new method for analysis of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) and Fourier‐Bessel (FB) expansion has been presented in this paper. The EMD decomposes an EEG signal into a finite set of band‐limited signals termed intrinsic mode functions (IMFs). The mean frequency (MF) for each IMF has been computed using FB expansion. The MF measure of the IMFs has been used as a feature in order to identify the difference between ictal and seizure‐free intracranial EEG signals. It has been shown that the MF feature of the IMFs has provided statistically significant difference between ictal and seizure‐free EEG signals. Simulation results are included to illustrate the effectiveness of the proposed method.

Highlights

Epileptic seizures are the outcome of the transient and unexpected electrical disturbance of the brain
The detection of epileptic seizures in the EEG signals is an important part in the diagnosis of epilepsy [1]
Mean frequency represents the centroid of the spectrum, and characterizes the frequency components of the intrinsic mode functions of the EEG signal

Summary

INTRODUCTION

Epileptic seizures are the outcome of the transient and unexpected electrical disturbance of the brain. Nonlinear methods have been proposed to extract new parameters linked to the electrical activity of the brain. Among these parameters, the Lyapunov exponent provides clinically useful information about the signal [2]; the correlation dimension techniques can contain information about the different neurological states of the brain [3]; the fractal dimension (FD) and entropy measure the complexity or the degree of disorder of the EEG signal [4, 5], while correlation integral, the measure sensitive to wide variety of nonlinearities, used in [6], could be used to characterize the epileptogenic regions of the brain during the interictal period. Unlike the sinusoidal basis functions in the Fourier transform, the Bessel functions are aperiodic, and decay over time These features of the Bessel functions make the FB series expansion suitable for analysis of nonstationary signals when compared to simple Fourier transform [11, 12]. The MF measure of the IMFs has been used as a feature in order to discriminate seizures from seizure-free intervals in intracranial EEG data recordings

EMPIRICAL MODE DECOMPOSITION

MEAN FREQUENCY COMPUTATION USING FOURIER-BESSEL EXPANSION

RESULTS AND DISCUSSION

CONCLUSION