A Library of Quantitative Markers of Seizure Severity
Abstract number :
1.198
Submission category :
3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year :
2022
Submission ID :
2204050
Source :
www.aesnet.org
Presentation date :
12/3/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:23 AM
Authors :
Sarah Gascoigne, BSc, PGDip – Newcastle University; Gabrielle Schroeder, PhD – School of Computing – Newcastle University; Leonard Waldmann, N/A – Newcastle University; Mariella Panagiotopoulou, BSc – School of Computing – Newcastle University; Fahmida Chowdhury, Dr – University College London Hospitals; Alison Cronie, Dr – NHS Greater Glasgow and Clyde; Beate Deihl, Dr – University College London Hospitals; John Duncan, Dr – University College London Hospitals; Jennifer Falconer, Dr – NHS Greater Glasgow and Clyde; Yu Guan, PhD – School of Computing – Newcastle University; Veronica Leach, Dr – NHS Greater Glasgow and Clyde; Shona Livingstone, Dr – NHS Greater Glasgow and Clyde; Christoforos Papasavvas, PhD – Newcastle University; Ryan Faulder, Dr – School of Medical Education – Newcastle University; Jessica Blickwedel, Dr – Newcastle University; Rhys Thomas, Dr – Newcastle University; Kevin Wilson, PhD – School of Mathematics, Statisitics and Physics – Newcastle University; Peter Taylor, PhD – School of Computing – Newcastle University; Yujiang Wang, Dr – School of Computing – Newcastle University
Rationale: Neuromodulation strategies may be able to diminish seizures making them less severe, such as preventing focal onset seizures from generalising. Current methods for grading seizure severity combine qualitative interpretations from patients and clinicians (Cramer and French, 2001). Quantitative measures of seizure severity would complement existing approaches, such as EEG monitoring, outcome monitoring, and seizure prediction. Therefore, we developed a library of quantitative electroencephalographic (EEG) markers that assess the spread and intensity of abnormal electrical activity during and after seizures.
Methods: We analysed intracranial EEG (iEEG) recordings from 63 patients and 1056 seizures. For each seizure, we computed markers of seizure severity that capture the signal magnitude, spread, and post-ictal suppression of seizures. All 16 markers were compared with ILAE seizure classifications to determine if they successfully distinguished subclinical, focal, and focal to bilateral tonic-clonic seizures. The differences between seizure types, as measured by severity markers, were investigated within and across patients. Circadian and longer-term rhythms in seizure severity markers were explored on a within-patient basis as further validation.
Results: Across all patients, 15 markers of seizure severity could distinguish focal and subclinical seizures across patients. In individual patients, 76.5% had a moderate to large difference (Wilcoxon rank sum r > 0.3) between focal and subclinical seizures in three or more markers. Circadian and longer-term changes in severity (Figure 1A-B) were found for 73% (Figure 1C) and 80% (Figure 1D) of patients, respectively.
Conclusions: We demonstrate the feasibility of using quantitative EEG markers to measure seizure severity. Quantitative markers can distinguish between seizure types and are therefore sensitive to established qualitative differences in seizure severity. We also provide evidence for modulations of seizure severity over different timescales. In future work, seizure severity markers may facilitate personalised, time-adaptive treatments or enhance seizure forecasting. We envisage that our proposed seizure severity library will be expanded and updated in collaboration with the epilepsy research community to include more measures and modalities.
Funding: The Engineering and Physical Sciences Research Council, Centre for Doctoral Training in Cloud Computing for Big Data (grant number EP/L015358/1), Wellcome Trust (208940/Z/17/Z), UKRI Future Leaders Fellowship (MR/V026569/1) and (MR/T04294X/1)
Neurophysiology