Title:Seizure Prediction on EEG Signals using Feature Augmentation based Multi Model Ensemble
Volume: 19
Issue: 1
Author(s): A. Anandaraj*P.J.A. Alphonse
Affiliation:
- Department of Computer Science and Engineering, Sri Eshwar College of Engineering, Coimbatore, India
Keywords:
Seizure prediction, epilepsy, ensemble modelling, multi-model ensemble, feature augmentation, feature engineering, feature selection.
Abstract:
Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due
to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid
in improving the lifestyle of epilepsy patients. After analyzing various patents related to seizure
prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients
is an important task for the early diagnosis of seizures.
Objective: The main objective of this paper is to assist epileptic patients to enhance their way of
living by predicting the seizure in advance.
Methods: This paper builds a feature augmentation-based multi-model ensemble-based architecture
for seizure prediction. The proposed technique is divided into 2 broad categories; feature
augmentation and ensemble modeling. The feature augmentation process builds temporal features
while the multi-model ensemble has been designed to handle the high complexity levels of the
EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous
classifier models. The second phase is based on the prediction results obtained from the first
phase. Experiments were performed using the seizure prediction dataset from the University Hospital
of Bonn.
Results: Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model.
High prediction levels indicate that the model is highly capable of providing accurate seizure
predictions, hence ensuring its applicability in real time.
Conclusion: The result of this paper has been compared with existing methods of predicting seizures
and it indicated that the proposed model has better enhancement in the accuracy levels.
