Tracking seizure cycles beats a prospective moving average

This commentary addresses the debate regarding the predictive value of multiday seizure cycles versus simple statistical baselines. Multidien seizure cyclicity is a prevalent, patient-specific phenomenon with promise for epilepsy management. We challenge the assertion that cycle tracking is no better than a 90-day moving average, which is an inherently retrospective model that lags changes in seizure likelihood. We compared a causal cyclic forecast to a prospectively applied moving average across a large seizure diary cohort (n=768) and two gold-standard chronic EEG cohorts (n=24). At the group level for the EEG and diary cohorts, cycle tracking demonstrated significantly superior accuracy to the moving average for both hourly and daily forecasts (p < 0.0001). These results confirm that event-based cyclical models offer more accurate, simulated real-world forecasts. We conclude that robust forecasting tools must prioritize the detection and modeling of seizure cycles to move beyond simple baseline performance and provide actionable clinical utility.