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.

本文介绍了两种基于 Transformer 架构的深度学习模型（TMC-T 和 TMC-ViT）用于癫痫发作预测，探讨了选取不同持续时间的 preictal 状态和样本数量对模型的影响，并通过使用 CHB-MIT 数据库中的原始 EEG 信号进行实验和统计验证，证明 TMC-ViT 模型优于卷积神经网络 (CNN) 等其他网络。