Detection of epileptic seizures from EEG signals with Hilbert Huang Transformation

Epilepsy is a significant neurological disease that occurs due to abnormal activities of particular portion brain neurons. Electroencephalography (EEG) signals are mainly used detect this disease. can be diagnosed automatically by measuring and analyzing the non-linearity non-stationary properties EEG signals. In study, Hilbert Huang Transformation (HHT) proposed extract distinctive features from for epileptic seizure detection. Research work, firstly, mean Instantaneous Amplitude (IA) Frequency (IF) data were extracted with as feature. Then, these classified Extreme Learning Machine (ELM). Classification results indicated seizures detected high accuracy. addition, performance evaluation method was compared some other techniques studied using same dataset recently. According experimental results, HHT based approach has 0.5-1% better classification accuracy than current studies higher in detecting similar studies.