ORCID
- Rohit Shankar: 0000-0002-1183-6933
Abstract
Objective: This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case–control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder). Methods: The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network-based [n = 4], and model-based [n = 2]) were calculated within each recording. Ensemble-based classifiers were developed using a two-tier cross-validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance. Results: We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance. Significance: These results provide evidence that the set of biomarkers could provide additional value to clinical decision-making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies.
DOI
10.1111/epi.18024
Publication Date
2024-05-23
Publication Title
Epilepsia
Volume
65
Issue
8
ISSN
0013-9580
Keywords
biomarker, case–control, computational, EEG, network, Electroencephalography/methods, Humans, Middle Aged, Child, Preschool, Male, Case-Control Studies, Young Adult, Epilepsy/diagnosis, Sensitivity and Specificity, Adolescent, Biomarkers, Female, Adult, Retrospective Studies, Aged, Child
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
First Page
2459
Last Page
2469
Recommended Citation
Woldman, W., Tait, L., Staniaszek, L., Galizia, E., Martin-Lopez, D., Walker, M., Azeez, A., Meiklejohn, K., Allen, D., Price, C., Georgiou, S., Bagary, M., Khalsa, S., Manfredonia, F., Tittensor, P., Lawthom, C., Howes, B., Shankar, R., & Terry, J. (2024) 'Estimating the likelihood of epilepsy from clinically noncontributory electroencephalograms using computational analysis: A retrospective, multisite case–control study', Epilepsia, 65(8), pp. 2459-2469. Available at: https://doi.org/10.1111/epi.18024
