title: Modeling the Complex Dynamics and Changing Correlations of Epileptic Events.
abstract: Patients with epilepsy can manifest short, sub-clinical epileptic "bursts" in addition to full-blown clinical seizures. We believe the relationship between these two classes of events-something not previously studied quantitatively-could yield important insights into the nature and intrinsic dynamics of seizures. A goal of our work is to parse these complex epileptic events into distinct dynamic regimes. A challenge posed by the intracranial EEG (iEEG) data we study is the fact that the number and placement of electrodes can vary between patients. We develop a Bayesian nonparametric Markov switching process that allows for (i) shared dynamic regimes between a variable number of channels, (ii) asynchronous regime-switching, and (iii) an unknown dictionary of dynamic regimes. We encode a sparse and changing set of dependencies between the channels using a Markov-switching Gaussian graphical model for the innovations process driving the channel dynamics and demonstrate the importance of this model in parsing and out-of-sample predictions of iEEG data. We show that our model produces intuitive state assignments that can help automate clinical analysis of seizures and enable the comparison of sub-clinical bursts and full clinical seizures.
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA.
authors.forename: Drausin F
authors.initials: DF
authors.lastname: Wulsin
authors.affiliation: Department of Statistics, University of Washington, Seattle, WA.
authors.forename: Emily B
authors.initials: EB
authors.lastname: Fox
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA ; Department of Neurology, University of Pennsylvania, Philadelphia, PA.
authors.forename: Brian
authors.initials: B
authors.lastname: Litt
keywords: Bayesian nonparametric; EEG; factorial hidden Markov model; graphical model; time series