Seizure forecasting by tracking cortical response to electrical stimulation

Seizure unpredictability is a significant burden in the lives of people with epilepsy. Previously published approaches to seizure forecasting analyzed intracranial electroencephalography (iEEG) recordings and showed that seizures can be forecast above chance levels. Although passive observation of the brain might provide some insights, repeated active perturbation of the cortex and measuring the cortical response may provide more direct information about time-varying cortical excitability. The aim of this study is to investigate whether seizures can be forecast by stimulating the cortex via intracranial electrodes and measuring cortical response from the iEEG.

We studied a cohort of eight patients with treatment-resistant epilepsy who were admitted to King's College Hospital for presurgical evaluation with iEEG. During their stay, they underwent prolonged single-pulse electrical stimulation for ~1 day. Stimuli were delivered every 5 min to a constant pair of electrodes, and all patients experienced at least one clinical seizure during the period of stimulation. We extracted quantitative features from the iEEG post-stimulus response and developed a LR algorithm to estimate the seizure likelihood at each stimulus. To evaluate the algorithm's performance, we used improvement over chance (IoC), sensitivity, time spent in warning, and Brier Skill Score. We also compared performance with seizure prediction based on passive observation of iEEG.

In seven of eight patients, seizures could be forecast using the post-stimulus response above chance levels (average IoC: 0.74). In comparison, the seizure forecasting performance based on passive (unstimulated) iEEG was less good (average IoC: 0.54).

These results suggest that cortical response to electrical stimulation may aid in the development of seizure forecasting algorithms as well as in the design of novel implantable devices that deliver electrical stimulation to control seizures.