Identifying Seizure Onset Zone From the Causal Connectivity Inferred Using Directed Information

In this paper, we developed a model-based and a data-driven estimator for directed information (DI) to infer the causal connectivity graph between electrocorticographic (ECoG) signals recorded from brain and to identify the seizure onset zone (SOZ) in epileptic patients. DI, an information theoretic quantity, is a general metric to infer causal connectivity between time series and is not restricted to a particular class of models unlike the popular metrics based on Granger causality or transfer entropy. The proposed estimators are shown to be almost surely convergent. Causal connectivity between ECoG electrodes in five epileptic patients is inferred using the proposed DI estimators, after validating their performance on simulated data. We then proposed a model-based and a data-driven SOZ identification algorithm to identify SOZ from the causal connectivity inferre using the model-based and data-driven DI estimators, respectively. The data-driven SOZ identification outperforms the model-based SOZ identification algorithm when benchmarked against the visual analysis by neurologist, the current clinical gold standard. The causal connectivity analysis presented here is the first step toward developing novel nonsurgical treatments for epilepsy.