  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.
