Comparison of Different Models in the Detection of Epilepsy

Abstract

Electroencephalograms (EEGs) are commonly used to diagnose brain conditions such as epilepsy. Deep Learning Algorithms have proven to be effective in analyzing EEG signals for detecting epileptic seizures. In this study, machine learning model: Random Forest Tree (RFT), deep learning models: Convolutional Neural Network (CNN) and ChronoNet, and transformer models: Vision Transformer (ViT) and Swin Transformer (ST) have been proposed to distinguish between epileptic individuals and healthy individuals. In Random Forest Tree, features are chosen randomly, data are trained in different trees and predictions from all trees are combined to get the final prediction. ChronoNet is popular for predicting the future value of time-series-based data.  ViT is a neural network architecture designed for image recognition tasks. Unlike traditional convolutional neural networks (CNNs), which rely on hand-designed feature extraction layers, ViTs use self-attention mechanisms to learn relevant image features directly from raw pixel values. Swin transformer is a vision transformer (ViT) variant but with a hierarchical way of processing the image. ChronoNet has performed best among the models with accuracies of 94% and 88.7% for the Guinea-Bissau and Nigeria datasets respectively. Transformer models have poor accuracies in this study, as only the first 3 channels out of 14 channels of the 10-20 system have been considered to create images from signals because of the scarcity of resources. Compared to the Swin Transformer model, the Vision Transformer architecture has shown better performance in accurately classifying epileptic patients and healthy individuals. Performances of RFT and CNN models have been satisfactory as well. Models have been trained and tested on publicly available data.