State space modeling of multidien cyclical progression of epilepsy

The risk of seizures in epilepsy fluctuates in cycles with multiday periodicity. The strength of these patient-specific seizure risk cycles can be modulated by disease processes. There is a lack of computational models of epilepsy that describe the progression and modulation of multiday seizure risk cycles. We developed a state space model (SSM) for epilepsy progression that learns individualized multiday seizure risk cycles from intracranial EEG (iEEG) data. To capture the cyclical nature of seizure risk, our model incorporated cyclical dynamics by using a special rotation matrix structure for the state transition matrix. The model learned patient-specific multiday cycles using a novel expectation-maximization algorithm. We evaluated the model on real-world data from one of the longest continuous iEEG recordings in people with epilepsy. The model forecast iEEG and inferred periods of heightened risk of seizures better than or comparable to baseline models, and provided novel insight into biological factors that modulate seizure risk cycles. To demonstrate the value of the model in developing brain stimulation treatment, the proposed SSM was integrated with reinforcement learning to reduce seizure risk in silico. Our model holds significant potential for addressing clinically important problems.