ArticleThe annals of applied statistics2023
BAYESIAN NON-HOMOGENEOUS HIDDEN MARKOV MODEL WITH VARIABLE SELECTION FOR INVESTIGATING DRIVERS OF SEIZURE RISK CYCLING.
Article in The annals of applied statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- Markov switching zero-inflated space-time multinomial models for comparing multiple infectious diseases.Biostatistics (Oxford, England) · 2025Article
- State-dependent effects of responsive neurostimulation depend on seizure localization.Brain : a journal of neurology · 2025Article
- A Bayesian switching linear dynamical system for estimating seizure chronotypes.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
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6 authors at 3 institutions in 1 country.
Funding
Abstract
A major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies, which are a stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data. For inference, we implement an efficient sampler that employs stochastic search and data augmentation techniques. We evaluate model performance on simulated seizure count data. We then demonstrate the clinical utility of the proposed model by analyzing daily seizure count data from 133 patients with Dravet syndrome collected through the
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