Automated feature extraction of epileptic EEG using Approximate Entropy

The disease epilepsy is characterized by a sudden and recurrent malfunction of the brain that is termed seizer. The electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. Nonlinear analysis quantifies the EEG signal to address randomness and predictability of brain activity. In this study we evaluate differences between epileptic EEG and normal EEG by computing Approximate Entropy (ApEn). The methodology is applied to two different EEG signals: 1) Normal 2) Epileptic. ApEn were calculated. The effectiveness of ApEn in comparison between two signals is investigated. It is observed that values of ApEn drops during an epileptic seizures.