Tracking epileptic seizure activity via information theoretic graphs

Yonathan Murin; Jeremy Kim; Andrea Goldsmith

doi:10.1109/ACSSC.2016.7869109

Tracking epileptic seizure activity via information theoretic graphs

Yonathan Murin
, Jeremy Kim
, Andrea Goldsmith

Stony Brook University

Stanford University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	583-587
Number of pages	5
ISBN (Electronic)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869109
State	Published - Mar 1 2017
Event	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: Nov 6 2016 → Nov 9 2016

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	0
ISSN (Print)	1058-6393
ISSN (Electronic)	2576-2303

Conference

Conference	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/Territory	United States
City	Pacific Grove
Period	11/6/16 → 11/9/16

Access to Document

10.1109/ACSSC.2016.7869109

Cite this

Murin, Y., Kim, J., & Goldsmith, A. (2017). Tracking epileptic seizure activity via information theoretic graphs. In M. B. Matthews (Ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 583-587). Article 7869109 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 0). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869109

@inproceedings{edd994cbe73d4ef2a64978802f81870b,

title = "Tracking epileptic seizure activity via information theoretic graphs",

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.",

author = "Yonathan Murin and Jeremy Kim and Andrea Goldsmith",

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doi = "10.1109/ACSSC.2016.7869109",

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Murin, Y, Kim, J & Goldsmith, A 2017, Tracking epileptic seizure activity via information theoretic graphs. in MB Matthews (ed.), Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016., 7869109, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 0, IEEE Computer Society, pp. 583-587, 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Pacific Grove, United States, 11/6/16. https://doi.org/10.1109/ACSSC.2016.7869109

Tracking epileptic seizure activity via information theoretic graphs. / Murin, Yonathan; Kim, Jeremy; Goldsmith, Andrea.
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. ed. / Michael B. Matthews. IEEE Computer Society, 2017. p. 583-587 7869109 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kim, Jeremy

AU - Goldsmith, Andrea

PY - 2017/3/1

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N2 - 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.

AB - 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.

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DO - 10.1109/ACSSC.2016.7869109

M3 - Conference contribution

AN - SCOPUS:85016270582

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

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BT - Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016

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Murin Y, Kim J, Goldsmith A. Tracking epileptic seizure activity via information theoretic graphs. In Matthews MB, editor, Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. IEEE Computer Society. 2017. p. 583-587. 7869109. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2016.7869109

Tracking epileptic seizure activity via information theoretic graphs

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