Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition

Epilepsy is a neurological brain dysfunction that is manifested by recrudescent seizures. Due to high temporal resolution, brain activities recorded by electroencephalography (EEG) are commonly used for localization of seizures and identification of epileptic dysfunctions. However, it is often time consuming and challenging to detect EEG seizures using conventional Fourier-based methods and manual interpretation due to nonlinear and nonstationary dynamics of EEG. In this paper, we propose a new framework based on Hilbert vibration decomposition (HVD) for discriminating normal and epileptic EEG recordings. HVD exploits Hilbert transform presentation of instantaneous frequency and extracts monocomponents that have distinctive time-varying amplitudes and instantaneous frequencies from nonstationary signals. The proposed method employs estimated instantaneous frequencies of largest energy components as features given to least squares support vector machine (LS-SVM) for recognizing epileptic seizures and is shown to be appealing for real time physiological signal processing applications due to its reduced computational complexity. Test results on a benchmark EEG data set achieved 97.66% classification accuracy and area of 0.9914 under the receiver operating characteristics (ROC) curve using the delta, theta and alpha rhythms.