Automatic identification and analysis of epileptic seizure

Epilepsy is a common neurological disease that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose epilepsy. This paper presents an automated diagnostic method for epileptic detection using gaussian mixture models based statistical time domain features. The statistical time domain namely root mean square (RMS), Sekwens (Sk), kurtosis (Ku), crest factor (CF) and clearance indicator (CLI) parameters are extracted. These features are used to train gaussian mixture models (GMM) for classification of EEG signals into three classes normal, pre-ictal and ictal. The GMM classifier is able to discriminate the three types of EEG signals and achieved better results compared to other existing techniques with an average accuracy of 98.75%. The proposed work can be used for automatic diagnosis of seizure onsets which would help the patients to take appropriate precautions.