Characterization of EEGA comparative study
The Electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. Chaotic measures like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H) and entropy are used to characterize the signal. Results indicate that these nonlinear measures are good discriminators of normal and epileptic EEG signals. These measures distinguish epileptic EEG and alcoholic from normal EEG with an accuracy of more than 90%. The dynamical behavior is less random for alcoholic and epileptic compared to normal. This indicates less of information processing in the brain due to the hyper-synchronization of the EEG. Hence, the application of nonlinear time series analysis to EEG signals offers insight into the dynamical nature and variability of the brain signals. As a pre-analysis step, the EEG data is tested for nonlinearity using surrogate data analysis and the results exhibited a significant difference in the correlation dimension measure of the actual data and the surrogate data.
electroencephalogram correlation dimension lyapunov exponent hurst exponent entropy epilepsy