EEG-based automatic epilepsy diagnosis using the instantaneous frequency with sub-band energies

摘要

This paper presents a novel approach for classifying the electroencephalogram (EEG) signals as normal or abnormal. This method uses features derived from theinstantaneous frequency (IF) and energies of EEG signals in different spectral sub-bands. Results of applying the method to a database of real signals revealthat, for the given classification task, the selected features consistently exhibit a high degree of discrimination between the EEG signals collected from healthy and epileptic patients. The analysis of the effect of window length used during feature extraction indicates that features extracted from EEG segments as short as 5seconds achieve a high average total accuracy of 95.3%.