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Electroencephalographic (EEG) signal records the neuronal activity in the brain and it is used in the diagnosis of epileptic seizure activities. Human inspection of non-stationary EEG signal for diagnosing epilepsy is cumbersome, time-consuming and inaccurate. In this paper an effective automatic approach to detect epilepsy using two feature extraction techniques namely local neighbor gradient pattern (LNGP) and symmetrically weighted local neighbor gradient pattern (SWLNGP) are proposed. Extracted features are fed into machine learning algorithms like k-nearest neighbor (k-NN), quadratic linear discriminant analysis, support vector machine, ensemble classifier and artificial neural network (ANN) to classify the EEG signals. In this study, the classification performance for 17 different cases using 10-fold cross validation with the following classification problems are executed (i) healthy-ictal, (ii) interictal-ictal, (iii) healthy-interictal, (iv) seizure free-ictal and (v) healthy-interictal-ictal. The experimental result shows that in all the cases LNGP and SWLNGP attained higher classification accuracy using ANN. Further, the computational performance and the classification accuracy of the proposed methods are compared with the recently proposed techniques for epileptic detection. It shows that the performance of LNGP and SWLNGP method with ANN classifier are superior over other recently proposed techniques for the aforesaid problems. Hence, the proposed methods are simple, fast, reliable and easily implementable for real-time epileptic detection.

In the past decades, wavelet transforms are widely employed for characterizing the electroencephalogram (EEG) signals for automatic diagnosis of epileptic seizure. But few vital issues like the classification of epileptic seizure types from normal EEG signals has not yet been benefited with wavelet transforms. Hence, in this paper, the two major types of wavelet transform, namely discrete wavelet transform (DWT) and wavelet packet decomposition (WPD) are employed for the automatic diagnosis of the epileptic seizure and its types. The publicly available KITS EEG database consisting of three groups namely, normal, focal epilepsy and generalized epilepsy are utilized in this work. Four experimental cases namely (i) normal-generalized epilepsy, (ii) normal-focal epilepsy, (iii) normal-focal-generalized and (iv) normal-epilepsy are used to investigate the proposed approach. Further, this paper attempts to identify the best wavelet function from the commonly used seven wavelet families and the level of decomposition required to analyse the EEG signals. The nine statistical features are extracted from the wavelet coefficients and fed into the support vector machine (SVM) classifier. From the experimental result it was found out that the DWT with rbio1.1 attained the highest classification accuracy for all the experimental cases. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed. [ABSTRACT FROM AUTHOR]