Localizing the Epileptic Seizure Onset Zone via Directed Information Graphs
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Localizing the Epileptic Seizure Onset Zone via Directed Information Graphs

Localizing the Epileptic Seizure Onset Zone via Directed Information Graphs

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David Packard Electrical Engineering, Room 202

350 Serra Mall

Stanford, CA 94305

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Abstract:

Epilepsy is one of the most common neurological disorders affecting about 1% of the world population. While in most cases treating epilepsy with antiepileptic drugs (AED) is successful, about a third of the patients cannot be adequately treated with AEDs. The main treatment for such patients is a surgical procedure for removal of the seizure onset zone (SOZ), the area in the brain from which the seizures originate. The main tool for accurately identifying the SOZ is electrocorticography (ECoG) recordings, taken from grids of electrodes placed on the cortex to allow a direct measurement of the brain’s electric activity. In this talk we will present a novel SOZ localization algorithm, based on ECoG recordings.

Our underlying hypothesis is that seizures start in the SOZ and then spread to surrounding areas in the brain. Thus, signals recorded at electrodes close to the SOZ should have a relatively large causal influence on the rest of the recorded signals. To evaluate the statistical causal influence between the recorded signals, we represent the set of electrodes using a directed graph, where the edges’ weights are the pair-wise causal influence, quantified via the information theoretic functional of directed information. The directed information is estimated from the ECoG recording using the nearest-neighbor estimation paradigm. Finally, the SOZ is inferred from the obtained network via a variation of the famous PageRank algorithm. Testing the proposed algorithm on 15 ECoG recordings of epileptic patients, listed in the iEEG portal, shows a close match with the SOZ estimated by expert neurologists.


Speaker Bio:

Yonathan Morin received his B.Sc. degree in Electrical Engineering and Computer Science from Tel-Aviv University, Israel, in 2004, and his M.Sc. and Ph.D. degrees in 2011 and 2015, respectively, from Ben-Gurion University of the Negev, Israel, both in Electrical Engineering. He is currently a postdoctoral scholar in the Wireless Systems Lab at Stanford University. From 2004 to 2010, he worked as a DSP and algorithms engineer, and as a team leader at Comsys Communication and Signal Processing. His research interests include network information theory and wireless communications, coding theory, molecular communications, and signal processing for neuroscience.

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David Packard Electrical Engineering, Room 202

350 Serra Mall

Stanford, CA 94305

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