EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers

TL;DR

This paper introduces Transformer-based deep learning models for predicting epileptic seizures from EEG signals, demonstrating that the Vision Transformer outperforms traditional CNNs in accuracy and effectiveness.

Contribution

The study develops novel multi-channel Transformer architectures for seizure prediction and evaluates the impact of preictal duration and sample size on model performance.

Findings

TMC-ViT outperforms CNN in seizure prediction accuracy.

Model performance varies with preictal duration.

Patient-specific training enhances prediction results.

Abstract

Epilepsy is one of the most common neurological diseases, characterized by transient and unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary method used to perform both the diagnosis and the monitoring of epilepsy. Given the unexpected nature of an epileptic seizure, its prediction would improve patient care, optimizing the quality of life and the treatment of epilepsy. Predicting an epileptic seizure implies the identification of two distinct states of EEG in a patient with epilepsy: the preictal and the interictal. In this paper, we developed two deep learning models called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer (TMC-ViT), adaptations of Transformer-based architectures for multi-channel temporal signals. Moreover, we accessed the impact of choosing different preictal duration, since its length is not a consensus among experts, and also evaluated how the sample size benefits each model. Our models are compared with fully connected, convolutional, and recurrent networks. The algorithms were patient-specific trained and evaluated on raw EEG signals from the CHB-MIT database. Experimental results and statistical validation demonstrated that our TMC-ViT model surpassed the CNN architecture, state-of-the-art in seizure prediction.