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Prediction of epilepsy development in traumatic brain injury patients from diffusion weighted MRI

Published: 30 June 2020 Publication History

Abstract

Post-traumatic epilepsy (PTE) is a life-long complication of traumatic brain injury (TBI) and is a major public health problem that has an estimated incidence that ranges from 2% -- 50%, depending on the severity of the TBI. Currently, the patho-mechanism that induces epileptogenesis in TBI patients is unclear, and one of the most challenging goals in the epilepsy community is to predict which TBI patients will develop epilepsy. In this work, we used diffusion weighted imaging (DWI) of 14 TBI patients recruited in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) to measure and analyze fractional anisotropy (FA), obtained from tract-based spatial statistic (TBSS) analysis. Then we used these measurements to train two support vector machine (SVM) models to predict which TBI patients have developed epilepsy. Our approach, tested on these 14 patients with a leave-two-out cross-validation, allowed us to obtain an accuracy of 0.857 ± 0.18 (with a 95% level of confidence), demonstrating it to be potentially promising for the early characterization of PTE.

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References

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Rachael Garner, Marianna La Rocca, Paul Vespa, Nigel Jones, Martin M Monti, Arthur W Toga, and Dominique Duncan. 2019. Imaging biomarkers of posttraumatic epileptogenesis. Epilepsia 60, 11 (2019), 2151--2162.
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Cited By

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  • (2024)Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging dataComputerized Medical Imaging and Graphics10.1016/j.compmedimag.2024.102386115(102386)Online publication date: Jul-2024
  • (2021)Lesion Normalization and Supervised Learning in Post-traumatic Seizure Classification with Diffusion MRIComputational Diffusion MRI10.1007/978-3-030-87615-9_12(133-143)Online publication date: 25-Sep-2021

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Published In

cover image ACM Other conferences
PETRA '20: Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments
June 2020
574 pages
ISBN:9781450377737
DOI:10.1145/3389189
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • NSF: National Science Foundation
  • CSE@UTA: Department of Computer Science and Engineering, The University of Texas at Arlington
  • NCRS: Demokritos National Center for Scientific Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 June 2020

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Author Tags

  1. diffusion tensor imaging
  2. epilepsy prediction
  3. support vector machine
  4. tract-based spatial statistic
  5. traumatic brain injury

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PETRA '20
Sponsor:
  • NSF
  • CSE@UTA
  • NCRS

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Cited By

View all
  • (2024)Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging dataComputerized Medical Imaging and Graphics10.1016/j.compmedimag.2024.102386115(102386)Online publication date: Jul-2024
  • (2021)Lesion Normalization and Supervised Learning in Post-traumatic Seizure Classification with Diffusion MRIComputational Diffusion MRI10.1007/978-3-030-87615-9_12(133-143)Online publication date: 25-Sep-2021

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