Chaotic Analysis of Epileptic EEG Signals

Electroencephalogram (EEG) is the recording of the electrical activity of the brain. One of the major fields of application of this relatively cheap and non-invasive diagnostic technique is epilepsy, which affects almost 1% of the world’s population. Automatic seizure detection is very important in clinical practice and has to be achieved by analyzing the EEG signals. Inter-ictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after longterm monitoring became common in investigation of epileptic patients. In this paper, a comprehensive chaotic analysis of the normal, ictal and inter-ictal segments in EEG signals is studied using nonlinear dynamical parameters such as correlation dimension, fractal dimension exponent and entropies. These measures show distinct difference for normal, ictal and interictal segments in the EEG recordings. The results are further supported by ANOVA test which gives a p-value of less than 0.01 with 95% confidence. The results of the study done for two age groups of pediatric subjects, demonstrated the potential of these chaotic measures in quantifying and automatically detecting the presence of any seizure activity in the EEG recordings with high statistical significance.