  Accurate prediction of epileptic seizures could prove critical for improving
patient safety and quality of life in drug-resistant epilepsy. Although deep
learning-based approaches have shown promising seizure prediction performance
using scalp electroencephalogram (EEG) signals, substantial limitations still
impede their clinical adoption. Furthermore, identifying the optimal preictal
period (OPP) for labeling EEG segments remains a challenge. Here, we not only
develop a competitive deep learning model for seizure prediction but, more
importantly, leverage it to demonstrate a methodology to comprehensively
evaluate the predictive performance in the seizure prediction task. For this,
we introduce a CNN-Transformer deep learning model to detect preictal
spatiotemporal dynamics, alongside a novel Continuous Input-Output Performance
Ratio (CIOPR) metric to determine the OPP. We trained and evaluated our model
on 19 pediatric patients of the open-access CHB-MIT dataset in a
subject-specific manner. Using the OPP of each patient, preictal and interictal
segments were correctly identified with an average sensitivity of 99.31%,
specificity of 95.34%, AUC of 99.35%, and F1- score of 97.46%, while prediction
time averaged 76.8 minutes before onset. Notably, our novel CIOPR metric
allowed outlining the impact of different preictal period definitions on
prediction time, accuracy, output stability, and transition time between
interictal and preictal states in a comprehensive and quantitative way and
highlighted the importance of considering both inter- and intra-patient
variability in seizure prediction.
