Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods

Electroencephalography (EEG) is a common tool used for the detection of epileptic seizures. However, the visual analysis of long-term EEG recordings is characterized by its subjectivity, time-consuming procedure and its erroneous detection. Various epileptic seizure detection algorithms have been proposed to deal with such issues. In this study, a novel automatic seizure-detection approach is proposed. Three different strategies are suggested to the user whereby he/she could choose the appropriate one for a given classification problem. 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). The classification procedure is executed using a support vector machine (SVM). 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. Our results show high performance in terms of accuracy (ACC), sensitivity (SEN) and specificity (SPE) compared to the state-of-the-art approaches. 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.