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Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes

(2018) NEUROIMAGE-CLINICAL. 17. p.10-15
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Abstract
Objective: To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity. Methods: Resting-state high-density EEG recording data from 20 left TLE patients, 20 right TLE patients and 35 healthy controls was used. Epochs without interictal spikes were selected. The cortical source activity was obtained for 82 regions of interest and whole-brain directed functional connectivity was estimated in the theta, alpha and beta frequency bands. These connectivity values were then used to build a classification system based on two two-class Random Forests classifiers: TLE vs healthy controls and left vs right TLE. Feature selection and classifier training were done in a leave-one-out procedure to compute the mean classification accuracy. Results: The diagnosis and lateralization classifiers achieved a high accuracy (90.7% and 90.0% respectively), sensitivity (95.0% and 90.0% respectively) and specificity (85.7% and 90.0% respectively). The most important features for diagnosis were the outflows from left and right medial temporal lobe, and for lateralization the right anterior cingulate cortex. The interaction between features was important to achieve correct classification.
Keywords
Temporal lobe epilepsy, Diagnosis, Lateralization, EEG, Machine learning, PARTIAL DIRECTED COHERENCE, FUNCTIONAL CONNECTIVITY, EEG, CLASSIFICATION

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MLA
Verhoeven, Thibault, et al. “Automated Diagnosis of Temporal Lobe Epilepsy in the Absence of Interictal Spikes.” NEUROIMAGE-CLINICAL, vol. 17, 2018, pp. 10–15, doi:10.1016/j.nicl.2017.09.021.
APA
Verhoeven, T., Coito, A., Plomp, G., Thomschewski, A., Pittau, F., Trinka, E., … van Mierlo, P. (2018). Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes. NEUROIMAGE-CLINICAL, 17, 10–15. https://doi.org/10.1016/j.nicl.2017.09.021
Chicago author-date
Verhoeven, Thibault, Ana Coito, Gijs Plomp, Aljoscha Thomschewski, Francesca Pittau, Eugen Trinka, Roland Wiest, et al. 2018. “Automated Diagnosis of Temporal Lobe Epilepsy in the Absence of Interictal Spikes.” NEUROIMAGE-CLINICAL 17: 10–15. https://doi.org/10.1016/j.nicl.2017.09.021.
Chicago author-date (all authors)
Verhoeven, Thibault, Ana Coito, Gijs Plomp, Aljoscha Thomschewski, Francesca Pittau, Eugen Trinka, Roland Wiest, Karl Schaller, Christoph Michel, Margitta Seeck, Joni Dambre, Serge Vulliemoz, and Pieter van Mierlo. 2018. “Automated Diagnosis of Temporal Lobe Epilepsy in the Absence of Interictal Spikes.” NEUROIMAGE-CLINICAL 17: 10–15. doi:10.1016/j.nicl.2017.09.021.
Vancouver
1.
Verhoeven T, Coito A, Plomp G, Thomschewski A, Pittau F, Trinka E, et al. Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes. NEUROIMAGE-CLINICAL. 2018;17:10–5.
IEEE
[1]
T. Verhoeven et al., “Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes,” NEUROIMAGE-CLINICAL, vol. 17, pp. 10–15, 2018.
@article{8540003,
  abstract     = {{Objective: To diagnose and lateralise temporal lobe epilepsy (TLE) by building a classification system that uses directed functional connectivity patterns estimated during EEG periods without visible pathological activity.

Methods: Resting-state high-density EEG recording data from 20 left TLE patients, 20 right TLE patients and 35 healthy controls was used. Epochs without interictal spikes were selected. The cortical source activity was obtained for 82 regions of interest and whole-brain directed functional connectivity was estimated in the theta, alpha and beta frequency bands. These connectivity values were then used to build a classification system based on two two-class Random Forests classifiers: TLE vs healthy controls and left vs right TLE. Feature selection and classifier training were done in a leave-one-out procedure to compute the mean classification accuracy.

Results: The diagnosis and lateralization classifiers achieved a high accuracy (90.7% and 90.0% respectively), sensitivity (95.0% and 90.0% respectively) and specificity (85.7% and 90.0% respectively). The most important features for diagnosis were the outflows from left and right medial temporal lobe, and for lateralization the right anterior cingulate cortex. The interaction between features was important to achieve correct classification.}},
  author       = {{Verhoeven, Thibault and Coito, Ana and Plomp, Gijs and Thomschewski, Aljoscha and Pittau, Francesca and Trinka, Eugen and Wiest, Roland and Schaller, Karl and Michel, Christoph and Seeck, Margitta and Dambre, Joni and Vulliemoz, Serge and van Mierlo, Pieter}},
  issn         = {{2213-1582}},
  journal      = {{NEUROIMAGE-CLINICAL}},
  keywords     = {{Temporal lobe epilepsy,Diagnosis,Lateralization,EEG,Machine learning,PARTIAL DIRECTED COHERENCE,FUNCTIONAL CONNECTIVITY,EEG,CLASSIFICATION}},
  language     = {{eng}},
  pages        = {{10--15}},
  title        = {{Automated diagnosis of temporal lobe epilepsy in the absence of interictal spikes}},
  url          = {{http://doi.org/10.1016/j.nicl.2017.09.021}},
  volume       = {{17}},
  year         = {{2018}},
}

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