This paper presents a probabilistic-graphical model that can be used to infer characteristics of instantaneous brain activity by jointly analyzing spatial and temporal dependencies observed in electroencephalograms (EEG). Specifically, we describe a factor-graph-based model with customized factor-functions defined based on domain knowledge, to infer pathologic brain activity with the goal of identifying seizure-generating brain regions in epilepsy patients. We utilize an inference technique based on the graph-cut algorithm to exactly solve graph inference in polynomial time. We validate the model by using clinically collected intracranial EEG data from 29 epilepsy patients to show that the model correctly identifies seizure-generating brain regions. Our results indicate that our model outperforms two conventional approaches used for seizure-onset localization (5-7% better AUC: 0.72, 0.67, 0.65) and that the proposed inference technique provides 3-10% gain in AUC (0.72, 0.62, 0.69) compared to sampling-based alternatives.
Yogatheesan Varatharajah (University of Illinois at Urbana Champaign)
Min Jin Chong (University of Illinois at Urbana-Champaign)
Krishnakant Saboo (University of Illinois Urbana Champaign)
I am a PhD candidate in Electrical and Computer Engineering at University of Illinois, Urbana-Champaign. Prior to that, I got a B.Tech. and M.Tech. degree in Electrical Engineering from Indian Institute of Technology, Bombay. I am broadly interested in topics at the intersection of machine learning, neurology, and neuroscience. My research leverages machine learning to understand neurological disorders and to develop tools for diagnosing and treating them.
Brent M Berry (Mayo Clinic)
Benjamin Brinkmann (Mayo Clinic)
Gregory Worrell (Mayo Clinic, Rochester)
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