Automated noninvasive seizure detection and localization using switching markov models and convolutional neural networks

Abstract

We introduce a novel switching Markov model for combined epileptic seizure detection and localization from scalp electroencephalography (EEG). Using a hierarchy of Markov chains to fuse multichannel information, our model detects seizure onset, localizes the seizure focus, and tracks seizure activity as it spreads across the cortex. This model-based seizure tracking and localization is complemented by a nonparametric EEG likelihood using convolutional neural networks. We learn our model with an expectation-maximization algorithm that uses loopy belief propagation for approximate inference. We validate our model using leave one patient out cross validation on EEG acquired from two hospitals. Detection is evaluated on the publicly available Children’s Hospital Boston dataset. We validate both the detection and localization performance on a focal epilepsy dataset collected at Johns Hopkins Hospital. To the best of our knowledge, our model is the first to perform automated localization from scalp EEG across a heterogeneous patient cohort.