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Prediction of Seizure Spread Network via Sparse Representations of Overcomplete Dictionaries

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Brain Informatics and Health (BIH 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9919))

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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.

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Authors and Affiliations

  1. Department of Industrial, Manufacturing and Systems Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA

    Feng Liu & Shouyi Wang

  2. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA

    Wei Xiang

  3. Neurological Surgery, University of Texas Southwestern, Dallas, TX, 75253, USA

    Bradley Lega

Authors
  1. Feng Liu
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  2. Wei Xiang
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  3. Shouyi Wang
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  4. Bradley Lega
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Corresponding author

Correspondence to Feng Liu .

Editor information

Editors and Affiliations

  1. George Mason University, Fairfax, Virginia, USA

    Giorgio A. Ascoli

  2. Department of Modeling, Analysis and Theory, Allen Institute for Brain Science, Seattle, Washington, USA

    Michael Hawrylycz

  3. University of Nebraska Omaha, Omaha, Nebraska, USA

    Hesham Ali

  4. Information Science and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Deepak Khazanchi

  5. College of Information Sciences and Technology, University of Nebraska Omaha, Omaha, Nebraska, USA

    Yong Shi

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Liu, F., Xiang, W., Wang, S., Lega, B. (2016). Prediction of Seizure Spread Network via Sparse Representations of Overcomplete Dictionaries. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_26

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  • DOI: https://doi.org/10.1007/978-3-319-47103-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47102-0

  • Online ISBN: 978-3-319-47103-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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