Connectivity analysis of ECoG in the initial, middle and terminal phase of epileptic seizure

• TANAKA SOICHIRO

  Graduate School of Biology-Oriented Science and Technology , Kindai University

• YOSHIDA HISASHI

  Department of Computational Systems Biology , Faculty of Biology-Oriented Science and Technology , Kindai Uni-versity

• MIYAUCHI MASAHARU

  Department of Neurosurgery, Faculty of Medicine, Kindai University

• NAKANO NAOKI

  Department of Neurosurgery, Faculty of Medicine, Kindai University

• KATO AMAMI

  Department of Neurosurgery, Faculty of Medicine, Kindai University

Bibliographic Information

Other Title

• てんかん発作初期・中期・収束期における皮質脳波コネクティビティ解析

Abstract

Recently, we proposed a connectivity analysis using cross-correlation function which we realized to visualize quantitative connectivity structure from electrocorticogram (ECoG) of intractable epilepsy patients. The connectivity analysis is presented by the minimum spanning tree, whose edges are configured with connectivity strength which is defined by linear combination of maximum value of the correlation function and its delay time between the channels. In this report, the connectivity analysis was newly applied to ECoG data of initial, middle and terminal phase of two intractable temporal epilepsy patients Results of the connectivity analysis were in accordance with PET as well as physician's observation for the patient with improved prognosis. Also, results of tracking the average of the top 1% connectivity strength showed that the value was rising when epileptiform discharges propagated throughout the brain. However, it was reduced in despite the epileptiform activities continued. In the seizure termination, it increased again.

Journal

• Transactions of Japanese Society for Medical and Biological Engineering

  Transactions of Japanese Society for Medical and Biological Engineering Annual56 (Abstract), S236-S236, 2018

  Japanese Society for Medical and Biological Engineering

Details

• CRID: 1390001288070060544
• NII Article ID: 130007483781
• DOI: 10.11239/jsmbe.annual56.s236
• ISSN: 18814379
• ISSN: 1347443X
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed