  Objective: Epilepsy is one of the most prevalent neurological diseases among
humans and can lead to severe brain injuries, strokes, and brain tumors. Early
detection of seizures can help to mitigate injuries, and can be used to aid the
treatment of patients with epilepsy. The purpose of a seizure prediction system
is to successfully identify the pre-ictal brain stage, which occurs before a
seizure event. Patient-independent seizure prediction models are designed to
offer accurate performance across multiple subjects within a dataset, and have
been identified as a real-world solution to the seizure prediction problem.
However, little attention has been given for designing such models to adapt to
the high inter-subject variability in EEG data. Methods: We propose two
patient-independent deep learning architectures with different learning
strategies that can learn a global function utilizing data from multiple
subjects. Results: Proposed models achieve state-of-the-art performance for
seizure prediction on the CHB-MIT-EEG dataset, demonstrating 88.81% and 91.54%
accuracy respectively. Conclusions: The Siamese model trained on the proposed
learning strategy is able to learn patterns related to patient variations in
data while predicting seizures. Significance: Our models show superior
performance for patient-independent seizure prediction, and the same
architecture can be used as a patient-specific classifier after model
adaptation. We are the first study that employs model interpretation to
understand classifier behavior for the task for seizure prediction, and we also
show that the MFCC feature map utilized by our models contains predictive
biomarkers related to interictal and pre-ictal brain states.
