A Spatio-Temporal Model of Seizure Propagation in Focal Epilepsy.

We propose a novel Coupled Hidden Markov Model (CHMM) to detect and localize epileptic seizures in clinical multichannel scalp electroencephalography (EEG) recordings. Our model captures the spatio-temporal spread of a seizure by assigning a sequence of latent states (i.e. baseline or seizure) to each EEG channel. The state evolution is coupled between neighboring and contralateral channels to mimic clinically observed spreading patterns. Since the latent state space is exponential, a structured variational algorithm is developed for approximate inference. The model is evaluated on simulated and clinical EEG from two different hospitals. One dataset contains seizure recordings of adult focal epilepsy patients at the Johns Hopkins Hospital; the other contains publicly available non-specified seizure recordings from pediatric patients at Boston Children's Hospital. Our CHMM model outperforms standard machine learning techniques in the focal dataset and achieves comparable performance to the best baseline method in the pediatric dataset. We also demonstrate the ability to track seizures, which is valuable information to localize focal onset zones.