Summary
Objective
We report on temporally clustered seizures detected from continuous long-term ambulatory human electroencephalographic data. The objective was to investigate short-term seizure clustering, which we have termed bursting, and consider implications for patient care, seizure prediction, and evaluating therapies.
Methods
Chronic ambulatory intracranial EEG data collected for the purpose of seizure prediction were annotated to identify seizure events. A detection algorithm was used to identify bursts of events. Burst events were compared to non-burst events to evaluate event dispersion, duration and dynamics.
Results
Bursts of seizures were present in six of fifteen patients, and detections were consistent over long term monitoring (> 2 years). Seizures within bursts are highly overdispersed compared to non-burst seizures. There was a complicated relationship between bursts and clinical seizures, although bursts were associated with multi-modal distributions of seizure duration, and poorer predictive outcomes. For three subjects, bursts demonstrated distinctive pre-ictal dynamics compared to clinical seizures.
Significance
We have previously hypothesized that there are distinct physiological pathways underlying short and long duration seizures. Here we show that burst seizures fall almost exclusively within the short population of seizure durations; however, a short duration was not sufficient to induce or imply bursting. We can therefore conclude that in addition to distinct mechanisms underlying seizure duration, there are separate factors regulating bursts of seizures. We show that bursts were a robust phenomenon in our patient cohort, which were consistent with overdispersed seizure rates, suggesting long-memory dynamics.