Prediction of seizure spread network via sparse representations of overcomplete dictionaries

Feng Liu, Wei Xiang, Shouyi Wang, Bradley Lega

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

5 Scopus citations

Abstract

Epilepsy is one of the most common brain disorders and affect people of all ages. Resective surgery is currently the most effective overall treatment for patients whose seizures cannot be controlled by medications. Seizure spread network with secondary epileptogenesis are thought to be responsible for a substantial portion of surgical failures. However, there is still considerable risk of surgical failures for lacking of priori knowledge. Cortico-cortical evoked potentials (CCEP) offer the possibility of understanding connectivity within seizure spread networks to know how seizure evolves in the brain as it measures directly the intracranial electric signals. This study is one of the first works to investigate effective seizure spread network modeling using CCEP signals. The previous unsupervised brain network connectivity problem was converted into a classical supervised sparse representation problem for the first time. In particular, we developed an effective network modeling framework using sparse representation of over-determined features extracted from extensively designed experiments to predict real seizure spread network for each individual patient. The experimental results on five patients achieved prediction accuracy of about 70%, which indicates that it is possible to predict seizure spread network from stimulated CCEP networks. The developed CCEP signal analysis and network modeling approaches are promising to understand network mechanisms of epileptogenesis and have a potential to render clinicians better epilepsy surgical decisions in the future.

Original languageEnglish (US)
Title of host publicationBrain Informatics and Health - International Conference, BIH 2016, Proceedings
PublisherSpringer Verlag
Pages262-273
Number of pages12
Volume9919 LNAI
ISBN (Print)9783319471020
DOIs
StatePublished - 2016
EventInternational Conference on Brain Informatics and Health, BIH 2016 - Omaha, United States
Duration: Oct 13 2016Oct 16 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9919 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherInternational Conference on Brain Informatics and Health, BIH 2016
CountryUnited States
CityOmaha
Period10/13/1610/16/16

Keywords

  • Brain connectivity
  • CCEP
  • Feature selection
  • Seizure spread network
  • Sparse representation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, F., Xiang, W., Wang, S., & Lega, B. (2016). Prediction of seizure spread network via sparse representations of overcomplete dictionaries. In Brain Informatics and Health - International Conference, BIH 2016, Proceedings (Vol. 9919 LNAI, pp. 262-273). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9919 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-47103-7_26