Ictal EEG classification based on amplitude and frequency contours of IMFs

Electroencephalogram (EEG) signal serves is a powerful tool in epilepsy detection. This study decomposes intrinsic mode functions (IMFs) into amplitude envelope and frequency functions on a time-scale basis using the analytic function of Hilbert transform. IMFs results from the empirical mode decomposition of EEG signals. Features such as energy and entropy parameters were calculated from the amplitude contour of each IMF. Other features, such as interquartile range, mean absolute deviation and standard deviation are also computed for their instantaneous frequencies. Discriminative features were extracted using a large database to classify healthy and ictal EEG signals. Normal EEG segments were differentiated from the seizure attack in individual IMF features, multiple features with individual IMF, and individual features with multiple IMFs. Discriminating capability of three Cases was tested. (i) Artificial neural network and (ii) adaptive neuro-fuzzy inference system classification were used to identify EEG segments with seizure attacks. ANOVA was used to analyze statistical performance. Energy and entropy-based features of instantaneous amplitude and standard deviation of instantaneous frequency of IMF2 and IMF1 have 100% accuracy, sensitivity, and specificity. Good performance with a single feature that represents information of the whole data was obtained. The result involved less complicated computation than other time-frequency analysis techniques.