Investigation of Optimal Components and Parameters of the Incremental PCA-based LSTM Network for Detection of EEG Epileptic Seizure Events
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Date
2023-12
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Faculty of Science, Gombe State University (GSU), Nigeria
Abstract
Prediction of Epileptic seizures is highly imperative to improve the epileptic patient’s life.
Epileptic seizures occur due to brain cells excessive abnormal activity that leads to unprovoked
seizures and may occur without prior notice. Therefore, preventive measure that monitor and
alert the possible occurrence of the seizures is paramount. Commercial and clinical available
epileptic seizure computer aided detection system that utilized deep learning algorithms suffers
from many challenges. These challenges ranges from low accuracy and precision, sensitive to
artifacts and noise, among others. To enhance and increase the accuracy and optimal
performance of these networks, this paper endeavor to investigate various optimization algorithm
to optimized the network components and parameters in the developed incremental Principal
Components Analysis based Long Short-Term Memory (Inc-PCA-LSTM) network for the
detection and classification of Electroencephalograph (EEG) epileptic seizure signals based on
the big data scenario. The model proved to be effective in the characterization of seven seizure
events. The Adam, Elu, Orthogonal, and L1/L2 performed better than their counterparts in
optimization functions, activation functions, initialization functions, and regularisation
techniques respectively. The accuracy values of 97.5%, 97.5%, 98.4%, and 98.5% was obtained
for each of the mentioned core components receptively.
Description
Keywords
EEG, Epileptic Seizure, PCA, LSTM, Deep Learning