Nonstationary-epileptic-spike detection algorithm in EEG signal using SNEO

Purpose This correspondence presents the evaluation of nonstationary epileptic spike (ES) detection algorithm in the electroencephalogram (EEG) 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. Methods 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 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. Results The simulation results are presented to demonstrate that the choice of window function plays a significant role in the efficient detection of ESs. The computational complexity is found to be in trade-off relationship with the detection accuracy of algorithm. Conclusions It may be inferred that the real-time EEG signals (rat data) can be processed and analyzed using the proposed adaptive scheme for the ES detection, which supersedes the conventional techniques.