Figure 1. Individual seizure risk forecasting in one subject.

(a) Responsive Neurostimulation (RNS^®) System, comprising a cranially-implanted neurostimulator connected to two four-contact intracranial depth leads (shown, for example, in hippocampus, red) and/or cortical strip leads (shown unconnected) that provide chronic electroencephalography (cEEG). (b) From these recordings, the RNS System provides hourly counts of detections of interictal epileptiform activity (IEA) and electrographic seizures (not shown). (c-e) Entire test dataset from one subject (S7) showing input temporal features, output daily forecasts, and observed seizures. (c) Time-series of IEA averaged over one calendar day (‘daily IEA’), underlying multidien cycle, and electrographic seizures that serve as some of the input temporal features for the forecasting model. (d) Daily forecast of seizure probability (gradient-colored lines) at 24-hour horizon (D₊₁) generated by a model (grey arrow) trained on ten months of data (not shown) and run on seven months of held-out test data (shown here) using input variables from c. Higher forecasted probabilities (red) form days-long pro-ictal states (red shadow) during which daily probability of seizures is continuously above the expected probability, defined as the long-term average daily seizure frequency calculated over months of training data (‘E’, here 0·19 seizures per day). (e) Seizures observed during and outside of pro-ictal states over these seven months. (f) Average pro-ictal state illustrated by peak-aligned average probability forecasts (top) and corresponding temporal distribution of seizures (bottom, shown as stacked individual events and percentage of total count on y-axis). (g) Hourly forecasts of seizure probability based on hourly IEA and its circadian cycle (not shown) refining pro-ictal states into hours of relatively higher and lower seizure risk. BSS: Brier skill score. (h) Seizures observed over this period of nine days.