View in EDS

A Compact Graph Convolutional Network With Adaptive Functional Connectivity for Seizure Prediction.

Saved in:
Bibliographic Details
Title: A Compact Graph Convolutional Network With Adaptive Functional Connectivity for Seizure Prediction.
Authors: Wei, Boxuan1 (AUTHOR) weiboxuan@buaa.edu.cn, Xu, Lu1 (AUTHOR) xulu181221@buaa.edu.cn, Zhang, Jicong1 (AUTHOR) jicongzhang@buaa.edu.cn
Source: IEEE Transactions on Neural Systems & Rehabilitation Engineering. 2024, Vol. 32, p3531-3542. 12p.
Subjects: Functional connectivity, Graph connectivity, People with epilepsy, Feature extraction, Epilepsy, Electroencephalography
Abstract: Seizure prediction using EEG has significant implications for the daily monitoring and treatment of epilepsy patients. However, the task is challenging due to the underlying spatiotemporal correlations and patient heterogeneity. Traditional methods often use large-scale models with independent components to capture the spatial and temporal features of EEG separately or explore shared patterns among patients with the help of pre-defined functional connectivity. In this paper, we propose a compact model, called the graph convolutional network based on adaptive functional connectivity (AFC-GCN), for seizure prediction. The model can adaptively infer evolution of functional connectivity in epilepsy patients during seizures through data-driven methods and synchronously analyze spatiotemporal response of functional connectivity in multiple topologies. On CHB-MIT datasets, the experimental results demonstrate that AFC-GCN achieves accurate and robust performance with low complexity. (AUC: 0.9820, accuracy: 0.9815, sensitivity: 0.9802, FPR: 0.0172). The proposed method has the potential to predict seizure during daily monitoring. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Neural Systems & Rehabilitation Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 182094271
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Compact Graph Convolutional Network With Adaptive Functional Connectivity for Seizure Prediction.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wei%2C+Boxuan%22">Wei, Boxuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> weiboxuan@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Lu%22">Xu, Lu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xulu181221@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jicong%22">Zhang, Jicong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jicongzhang@buaa.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Neural+Systems+%26+Rehabilitation+Engineering%22">IEEE Transactions on Neural Systems & Rehabilitation Engineering</searchLink>. 2024, Vol. 32, p3531-3542. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Functional+connectivity%22">Functional connectivity</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+connectivity%22">Graph connectivity</searchLink><br /><searchLink fieldCode="DE" term="%22People+with+epilepsy%22">People with epilepsy</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Epilepsy%22">Epilepsy</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Seizure prediction using EEG has significant implications for the daily monitoring and treatment of epilepsy patients. However, the task is challenging due to the underlying spatiotemporal correlations and patient heterogeneity. Traditional methods often use large-scale models with independent components to capture the spatial and temporal features of EEG separately or explore shared patterns among patients with the help of pre-defined functional connectivity. In this paper, we propose a compact model, called the graph convolutional network based on adaptive functional connectivity (AFC-GCN), for seizure prediction. The model can adaptively infer evolution of functional connectivity in epilepsy patients during seizures through data-driven methods and synchronously analyze spatiotemporal response of functional connectivity in multiple topologies. On CHB-MIT datasets, the experimental results demonstrate that AFC-GCN achieves accurate and robust performance with low complexity. (AUC: 0.9820, accuracy: 0.9815, sensitivity: 0.9802, FPR: 0.0172). The proposed method has the potential to predict seizure during daily monitoring. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IEEE Transactions on Neural Systems & Rehabilitation Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=182094271
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/TNSRE.2024.3460348
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 3531
    Subjects:
      – SubjectFull: Functional connectivity
        Type: general
      – SubjectFull: Graph connectivity
        Type: general
      – SubjectFull: People with epilepsy
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Epilepsy
        Type: general
      – SubjectFull: Electroencephalography
        Type: general
    Titles:
      – TitleFull: A Compact Graph Convolutional Network With Adaptive Functional Connectivity for Seizure Prediction.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Wei, Boxuan
      – PersonEntity:
          Name:
            NameFull: Xu, Lu
      – PersonEntity:
          Name:
            NameFull: Zhang, Jicong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: 2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 15344320
          Numbering:
            – Type: volume
              Value: 32
          Titles:
            – TitleFull: IEEE Transactions on Neural Systems & Rehabilitation Engineering
              Type: main
ResultId 1