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We propose a novel Coupled Hidden Markov Model to detect epileptic seizuresin multichannel electroencephalography (EEG) data. Our model defines a networkof seizure propagation paths to capture both the temporal and spatial evolutionof epileptic activity. To address the intractability introduced by the coupledinteractions, we derive a variational inference procedure to efficiently inferthe seizure evolution from spectral patterns in the EEG data. We validate ourmodel on EEG aquired under clinical conditions in the Epilepsy Monitoring Unitof the Johns Hopkins Hospital. Using 5-fold cross validation, we demonstratethat our model outperforms three baseline approaches which rely on a classicaldetection framework. Our model also demonstrates the potential to localizeseizure onset zones in focal epilepsy.

A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models