Parsing Epileptic Events Using a Markov Switching Process for Correlated Time Series

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 stud-ied quantitatively—could yield important in-sights 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 num-bers 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. We demon-strate the importance of this model in pars-ing and out-of-sample predictions of iEEG data. We show that our model produces in-tuitive state assignments that can help auto-mate clinical analysis of seizures and enable the comparison of sub-clinical bursts and full clinical seizures