EEG driven seizure classification framework leveraging variational mode decomposition technique and entropy features based Bayesian optimized SVM

EEG signal can capture spatial and temporal shifts in electrical activity of the brain, thereby acting as a prominent biomarker to diagnose seizure. Nevertheless, as EEG recordings are generally high-dimensional and noisy, manual examination of any such abnormalities in EEG patterns to diagnose seizure is a time consuming and tedious task for a neurologist. Therefore, it is important to devise an effective decision support system which can uncover the abnormalities in the EEG signals. In this study, we present an EEG based seizure classification framework which leverages the potentials of variational mode decomposition (VMD) technique and a Bayesian optimized support vector machine (BOSVM) to classify the type of generalized motor and non-motor seizures. Unlike existing methods that rely on statistical or time–frequency features, this study exploits entropy metrics extracted from VMD-decomposed EEG modes for seizure classification. To select the most discriminative features, we harness both neighborhood component analysis (NCA) technique and ReliefF algorithm and apply feature fusion method to train the classifier models. For addressing the class imbalance problem in the dataset, an adaptive synthetic (ADASYN) sampling, which can augment the synthetic samples to minority classes, is adopted. Experiments conducted on a publicly available temple university hospital (TUH) dataset substantiate that the proposed framework achieves an average classification accuracy of 98.37% and offers improved generalization and robustness compared to the state-of-the-art machine learning classifier models.