This paper presents a new method to automate the process of epileptic seizure detection in electroencephalogram (EEG) signals using wavelet transform and an improved version of negative correlation learning (NCL) algorithm. An improved version of NCL is proposed by incorporating the capability of gating network, as a dynamic combining part of the mixture of experts (ME), into the combining outputs of base experts which are trained using negative correlation learning algorithm. The NCL training algorithm encourages the base experts to learn different parts or aspects of data set and the gating network provides the local competence of these base experts. Three types of normal (recorded from five healthy persons with eyes open), seizure-free (recorded from epileptogenic zoon of five patients) and epileptic EEG signals were decomposed into wavelet coefficients using discrete wavelet transform. Then the statistical features of the wavelet coefficients were computed representing them into the classifiers. Experimental results show that our proposed method classifies normal, seizure-free and epileptic EEG signals with the accuracy of 96.92% which is significantly better than previous combining methods.
In this study, a new approach based on the computation of fuzzy similarity index was presented for discrimination of electroencephalogram (EEG) signals. The EEG, a highly complex signal, is one of the most common sources of information used to study the brain function and neurological disorders. The analyzed EEG signals were consisted of five sets (set A - healthy volunteer, eyes open; set B - healthy volunteer, eyes closed; set C - seizure-free intervals of five patients from the hippocampal formation of the opposite hemisphere; set D - seizure-free intervals of five patients from the epileptogenic zone; set E - epileptic seizure segments). The EEG signals were considered as chaotic signals and this consideration was tested successfully by the computation of Lyapunov exponents. The computed Lyapunov exponents were used to represent the EEG signals. The aim of the study is discriminating the EEG signals by the combination of Lyapunov exponents and fuzzy similarity index. Toward achieving this aim, fuzzy sets were obtained from the feature sets (Lyapunov exponents) of the signals under study. The results demonstrated that the similarity between the fuzzy sets of the studied signals indicated the variabilities in the EEG signals. Thus, the fuzzy similarity index could discriminate the healthy EEG segments (sets A and B) and the other three types of segments (sets C, D, and E) recorded from epileptic patients.