Comparison of Hilbert Vibration Decomposition with Empirical Mode Decomposition for Classifying Epileptic Seizures

Epilepsy is a frequently seen neurological disorder manifested by repeating seizures. EEG signals of epilepsy patients are able to depict these seizures due to their high temporal resolution. However, it is generally challenging to differentiate these seizures by manual observation. Furthermore, Fourier based signal processing methods are unable to sufficiently analyze EEG signals as they are nonlinear and nonstationary by nature. Therefore, methods such as empirical mode decomposition (EMD) are exploited when working on epileptic EEG signals with the intention of detecting epileptic seizures. In this paper, we propose a framework in order to extract features from healthy, interictal and ictal EEG signals decomposed via EMD and Hilbert vibration decomposition (HVD), and then classify these signals with a convolutional neural network (CNN). Then, we evaluate the performance of both decomposition methods in detecting epileptic seizures. The obtained features are used for 10-fold cross validation with a CNN. The study was conducted on a benchmark dataset, where the EMD yielded 95.11% classification accuracy while HVD method achieved 100% accuracy. The overall performance of the HVD was found better compared to the EMD.