Abstract：The automatic seizure detection and classification are significant in both diagnosis of epilepsy and relieving heavy working load of doctors. In this paper we proposed a new seizure detection method based on multifeatures of longterm intracranial EEG. After wavelet and halfwave decomposition, differeutial variance, relative energy and relative fluctuation index were used to characterize seizure activity as three features. Then the feature vector was fed to Bayesian formulation which was used as a classifier. A sensitivity of 94.2%, average specificity of 95.6 % and a false detection rate of 1.16 per hour were achieved with longterm intracranial EEG from Freiburg dataset. The experimental results indicated that this method is able to detect epileptic seizures effectively and its low computational complexity made it suitable for realtime seizure detection.
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