  Seizures are one of the defining symptoms in patients with epilepsy, and due
to their unannounced occurrence, they can pose a severe risk for the individual
that suffers it. New research efforts are showing a promising future for the
prediction and preemption of imminent seizures, and with those efforts, a vast
and diverse set of features have been proposed for seizure prediction
algorithms. However, the data-driven discovery of nonsinusoidal waveforms for
seizure prediction is lacking in the literature, which is in stark contrast
with recent works that show the close connection between the waveform
morphology of neural oscillations and the physiology and pathophysiology of the
brain, and especially its use in effectively discriminating between normal and
abnormal oscillations in electrocorticographic (ECoG) recordings of epileptic
patients. Here, we explore a scalable, energy-guided waveform search strategy
on spatially-projected continuous multi-day ECoG data sets. Our work shows that
data-driven waveform learning methods have the potential to not only contribute
features with predictive power for seizure prediction, but also to facilitate
the discovery of oscillatory patterns that could contribute to our
understanding of the pathophysiology and etiology of seizures.
