Modeling the Complex Dynamics and Changing Correlations of Epileptic Events

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 subclinical bursts and full clinical seizures. Keywords: Bayesian nonparametric, EEG, factorial hidden Markov model, graphical model, time series 1. Introduction Despite over three decades of research, we still have very little idea of what defines a seizure.