Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. 1D-local binary pattern based feature extraction for classification of epileptic EEG signals. IEEE J Biomed Health Informatics 2017 21:888–96. Automated diagnosis of epilepsy using key-point-based local binary pattern of EEG signals. Tiwari AK, Pachori RB, Kanhangad V, Panigrahi BK. In: 2nd Int Image Process Appl Syst Conf 2017:1–6. Classification of epileptic cerebral activity using robust features and support vector machines. Mahjoub C, Chaibi S, Lajnef T, Kachouri A. Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Biomed Signal Process Control 2014 9:1–5. Classification of ictal and seizure-free EEG signals using fractional linear prediction. Epileptic EEG detection using the linear prediction error energy. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Informed consent: Informed consent is not applicable.Įthical approval: The conducted research is not related to either human or animal use. Research funding: Authors state no funding involved.Ĭonflict of interest: Authors state no conflict of interest. Thus, the proposed approach for automatic seizure detection can be considered as a valuable alternative to existing methods, able to alleviate the overload of visual analysis and accelerate the seizure detection. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. The performance of the proposed method is evaluated through a publicly available database from which six binary classification cases are formulated to discriminate between healthy, seizure and non-seizure EEG signals. The classification procedure is executed using a support vector machine (SVM). Indeed, the feature extraction step, including both linear and nonlinear measures, is performed either directly from the EEG signals, or from the derived sub-bands of tunable-Q wavelet transform (TQWT), or even from the intrinsic mode functions (IMFs) of multivariate empirical mode decomposition (MEMD). Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. In this study, a novel automatic seizure-detection approach is proposed. Various epileptic seizure detection algorithms have been proposed to deal with such issues. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Electroencephalography (EEG) is a common tool used for the detection of epileptic seizures.
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