Optimized Deep Learning Techniques for Enhanced Epilepsy Prediction from Electroencephalogram Signals
Ghaith Koushaji, Anandh Bose, Maria Abdulaziz Alrafi, Rafiulla Gilkaramenthi, Mohammad El-NablawayAbstract
Background:
Well over 65 million people throughout the world suffered from epilepsy, a common neurological illness. The success rate of standard medical interventions, including medication and surgery, is <30%. On the other hand, seizure prevention methods involving prompt therapeutic interventions are possible with early seizure prediction. Effective and highly accurate epilepsy prediction is still a challenge, despite experts’ best efforts.
Aims and Objectives:
We present the Particle Swarm Optimized–Convolutional Neural Network and Long Short Term Memory (PSO CNN LSTM) model for electroencephalogram (EEG) signal categorization to support epilepsy diagnosis.
Materials and Methods:
For EEG classification, we acquire EEG samples from the Bonn dataset that has been used in many reliable studies, which contains five categories of EEG signals, including both normal and epileptic samples. The dataset is preprocessed by removing artifacts and noise. The Short Time Fourier Transform method is subsequently employed for converting the EEG signals into spectrogram images. The postulated PSO CNN LSTM model and other deep learning models, such as CNN and CNN LSTM, employed these spectrogram images as inputs. MATLAB software was implemented for training and verifying models.
Results and Conclusions:
The studies’ findings revealed that the suggested PSO CNN LSTM model performs better than the other models, with an accuracy of 96% as opposed to 84% and 94%, respectively.