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A major issue in the clinical management of epilepsy is the unpredictabilityof seizures. Yet, traditional approaches to seizure forecasting and riskassessment in epilepsy rely heavily on raw seizure frequencies, which are astochastic measurement of seizure risk. We consider a Bayesian non-homogeneoushidden Markov model for unsupervised clustering of zero-inflated seizure countdata. The proposed model allows for a probabilistic estimate of the sequence ofseizure risk states at the individual level. It also offers significantimprovement over prior approaches by incorporating a variable selection priorfor the identification of clinical covariates that drive seizure risk changesand accommodating highly granular data. For inference, we implement anefficient sampler that employs stochastic search and data augmentationtechniques. We evaluate model performance on simulated seizure count data. Wethen demonstrate the clinical utility of the proposed model by analyzing dailyseizure count data from 133 patients with Dravet syndrome collected through theSeizure Tracker TM system, a patient-reported electronic seizure diary. Wereport on the dynamics of seizure risk cycling, including validation of severalknown pharmacologic relationships. We also uncover novel findingscharacterizing the presence and volatility of risk states in Dravet syndrome,which may directly inform counseling to reduce the unpredictability of seizuresfor patients with this devastating cause of epilepsy.

Bayesian Non-Homogeneous Hidden Markov Model with Variable Selection for Investigating Drivers of Seizure Risk Cycling