FCTA-CSTM: A Hybrid Optimized Deep Convolutional Neural Network and Long Short-Term Memory for Epileptic Seizure Prediction
Keywords:
Epileptic seizure, electroencephalography signals, Felidae Canis tracking algorithm, deep convolutional neural network and long short-term memory,deep learning.Abstract
Epileptic seizure is a nerve-wracking chronicle disease that occurs in older patients as well as middle-aged people, which is necessary to predict the disease sometimes it may cause serious health issues and lead to death in rare cases. Even though there are more methods for Epileptic seizure prediction, the computational efficiency and accurate results for prediction are very low. Hence a feasible deep learning (DL) based Felidae Canis tracking algorithm with a deep convolutional neural network and long short-term memory (FCTA-CSTM) model for predicting epileptic seizure is proposed. The electroencephalography (EEG) signals are used in this research for effective prediction. The FCTA-CSTM model employed with optimization mechanism, FACTA is the combination of three nature-inspired optimizers that intend to select the most suitable feature to train the model and improve the convergence speed as well as increase the potential of the model. Besides the model, CSTM trains effectively and accurately predict the disease which avails patients for early diagnosis. The performance of the model can be analyzed using accuracy, sensitivity, and specificity metrics and achieved 95.35%, 94.76%, and 95.94% respectively compared to other state-of-the-art methods.
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