Automatic Identification of Epileptic Eeg Signals Using Nonlinear Parameters

Epilepsy is a brain disorder causing people to have recurring seizures. Electroencephalogram (EEG) is the electrical activity of the brain signals that can be used to diagnose the epilepsy. The EEG signal is highly nonlinear and nonstationary in nature and may contain indicators of current disease, or warnings about impending diseases. The chaotic measures like correlation dimension (CD), Hurst exponent (H), and approximate entropy (ApEn) can be used to characterize the signal. These features extracted can be used for automatic diagnosis of seizure onsets which would help the patients to take appropriate precautions. These nonlinear features have been reported to be a promising approach to differentiate among normal, pre-ictal (background), and epileptic EEG signals. In this work, these features were used to train both Gaussian mixture model (GMM) and support vector machine (SVM) classifiers. The performance of the two classifiers were evaluated using the receiver operating characteristics (ROC) curves. Our results show that the GMM classifier performed better with average classification efficiency of 95%, sensitivity and specificity of 92.22% and 100%, respectively.