Detection of epileptic seizures using chaotic and statistical features in the EMD domain

An artificial neural network (ANN)-based method, using a combination of statistical and chaotic features, is proposed to discriminate electroencephalogram (EEG) signals for seizure detection. The EEG signals are subjected to empirical mode decomposition, generating intrinsic mode functions. Statistical and chaotic features such as skewness, kurtosis, variance, and largest Lyapunov exponent, correlation dimension and approximate entropy are extracted from these modes and fed to the ANN to classify the EEG signals. It is shown that the proposed method can achieve up to 100% accuracy as compared to several state-of-the-art techniques in discriminating the seizure signals from the non-seizure ones.