Tracking epileptic seizure activity via information theoretic graphs

Yonathan Murin, Jeremy Kim, Andrea Goldsmith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This work introduces an algorithm for localization of the seizure onset zone (SOZ) of epileptic patients based on electrocorticography (ECoG) recordings. The algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes, while the edge weights are the pair-wise causal influence. This causal influence is quantified by estimating the pair-wise directed information functional. The SOZ is inferred from the graph by identifying nodes which act as sources of causal influence. Results based on several datasets show a close match between the inferred SOZ and the SOZ estimated by expert neurologists.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages583-587
Number of pages5
ISBN (Electronic)9781538639542
DOIs
StatePublished - Mar 1 2017
Externally publishedYes
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
CountryUnited States
CityPacific Grove
Period11/6/1611/9/16

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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