Automatic seizure detection is significant for long-term monitoring of epilepsy, as well as for diagnostics and rehabilitation, and can decrease the duration of work required when inspecting the EEG signals. In this study we propose a novel method for feature extraction and pattern recognition of ictal EEG, based upon empirical mode decomposition (EMD) and support vector machine (SVM). First the EEG signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and then the coefficient of variation and fluctuation index of IMFs are extracted as features. SVM is then used as the classifier for recognition of ictal EEG. The experimental results show that this algorithm can achieve the sensitivity of 97.00% and specificity of 96.25% for interictal and ictal EEGs, and the sensitivity of 98.00% and specificity of 99.40% for normal and ictal EEGs on Bonn data sets. Besides, the experiment with interictal and ictal EEGs from Qilu Hospital dataset also yields a satisfactory sensitivity of 98.05% and specificity of 100%.
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