Epilepsy is caused by abnormal brain tissue activity and effecting parts of the
cerebral cortex to experience excessive synchronization. The main symptom of
epilepsy is seizure that attacks suddenly and occurs several times. Unpredictable
seizures allow the sufferer to not survive. Patients with epilepsy are still doing the
traditional and troublesome manual seizure prediction. Therefore, automatic seizure
prediction would help patients prepare for upcoming seizures in a short period of
time.
Seizures can be predicted by analyzing the recordings of electroencephalogram
(EEG) signals. The epileptic EEG recordings consists of three kinds of condition,
namely pre-ictal, ictal, and interictal. Seizure prediction is performed by detecting
the pre-ictal condition. Machine learning (ML) algorithms have the potential to
predict seizures as early and accurately as possible. In this study, the EEG signals
were extracted using the multiscale empirical wavelet transform (EWT) and
fluctuation-based dispersion entropy (FDispEn) methods. The dataset used is
Temple University Hospital EEG Seizure Corpus (TUSZ). The multiscale method
plays a role in the decomposition stage using EWT. Then, the features are extracted
from the decomposed signal using FDispEn. This research also compares the use
of EWT with empirical mode decomposition (EMD), and FDispEn with
permutation entropy (PE). Then, the features are classified with support vector
machine (SVM) to obtain the seizure prediction results.
The type of seizure studied was Generalized Non-specific Seizure (GNSZ) with
28 training data and 9 testing data. Performance of the methods was evaluated by
seizure prediction horizon (SPH), average false prediction rate (FPR), and
sensitivity (SE). The lowest mean FPR, highest SE, and longest SPH were obtained
by the EMD FDispEn method, which were 0.54 h 1 , 100%, and 2 minutes,
respectively. FDispEn is able to produce features that are more discriminatory and
have the potential for real-time applications. EWT is able to predict seizure
effectively, yet could not be more reliable than EMD.