Improved EEG Signal Analysis Techniques for Epileptic Spike Detection and Artifact Excision

Abstract

Electroencephalography is the most common noninvasive technique utilized for monitoring the electrical brain activity and inferring brain function, in which the recorded signal traces represent an electric signal from a large number of neurons. The main goal of electroencephalogram (EEG) signal analysis is to infer functional connectivity between different brain areas, which is directly useful for neuroscience and clinical investigations. Due to its potentially complex nature and due to the presence of epileptic-spikes (ESs), ocular-artifacts (OAs) and inevitable additive-white-Gaussian-noise (AWGN), the electroencephalogram signal processing poses some great challenges for researchers. These challenges can be tackled, by using the epileptic spike detection, artifact suppression and noise removal techniques, in a principled manner via adaptive signal processing approach. We first present the evaluation of nonstationary epileptic spike detection algorithm for the electroencephalogram signal using the smoothed-nonlinear-energy-operator (SNEO) based on the different time-domain window functions. However, the incorporation of adaptive threshold determination procedure enhances the performance of proposed ES detector. The detection procedure exploits the fact that the presence of instantaneous ES corresponds to the high instantaneous energy content at the high frequencies. In addition to the stochastic amplitude, sign and the location of appearance of triangular spikes in the synthetic EEG signal, its base-width is also considered to be variable for the nonstationary signal analysis. The five pairs of EEG signals, obtained from electrodes placed on the left and right frontal cortex of male adult WAG/Rij rats, are used for the testing of proposed adaptive scheme in the real-time environment, which is a genetic animal model of human epilepsy. The simulation results are presented to demonstrate that the choice of window function plays a pivotal role in the efficient detection of ESs.