Searching for waveforms on spatially-filtered epileptic ECoG

TL;DR

This paper introduces a scalable, energy-guided waveform search method on spatially-filtered ECoG data, aiming to discover novel oscillatory patterns for improved seizure prediction and understanding of epilepsy.

Contribution

It presents a new data-driven waveform discovery approach on multi-day ECoG data, linking waveform morphology to seizure prediction and brain physiology.

Findings

Waveform learning methods can identify features predictive of seizures.

Discovered oscillatory patterns may enhance understanding of seizure mechanisms.

Method shows potential for scalable analysis of long-term ECoG data.

Abstract

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.