Rationale: Currently, there is some ambiguity over the role of postictal generalized electro-encephalographic suppression (PGES) as a biomarker in sudden unexpected death in epilepsy (SUDEP). Visual analysis of PGES, known to be subjective, may account for this. In this study, we set out to perform an analysis of PGES presence and duration using a validated signal processing tool, specifically to examine the association between PGES and seizure features previously reported to be associated with visually analyzed PGES. Methods: This is a prospective, multicenter epilepsy monitoring study of autonomic and breathing biomarkers of SUDEP in adult patients with intractable epilepsy. We studied videoelectroencephalogram (vEEG) recordings of generalized convulsive seizures (GCS) in a cohort of patients in whom respiratory and vEEG recording were carried out during the evaluation in the epilepsy monitoring unit. A validated automated EEG suppression detection tool was used to determine presence and duration of PGES. Results: We studied 148 GCS in 87 patients. PGES occurred in 106/148 (71.6%) seizures in 70/87 (80.5%) of patients. PGES mean duration was 38.7 ± 23.7 (37; 1-169) seconds. Presence of tonic phase during GCS, including decerebration, decortication and hemi-decerebration, were 8.29 (CI 2.6-26.39, p = 0.0003), 7.17 (CI 1.29-39.76, p = 0.02), and 4.77 (CI 1.25-18.20, p = 0.02) times more likely to have PGES, respectively. In addition, presence of decerebration (p = 0.004) and decortication (p = 0.02), older age (p = 0.009), and hypoxemia duration (p = 0.03) were associated with longer PGES durations. Conclusions: In this study, we confirmed observations made with visual analysis, that presence of tonic phase during GCS, longer hypoxemia, and older age are reliably associated with PGES. We found that of the different types of tonic phase posturing, decerebration has the strongest association with PGES, followed by decortication, followed by hemi-decerebration. This suggests that these factors are likely indicative of seizure severity and may or may not be associated with SUDEP. An automated signal processing tool enables objective metrics, and may resolve apparent ambiguities in the role of PGES in SUDEP and seizure severity studies.
Zhao, Xiuhe; Vilella, Laura; Zhu, Liang; Rani, M R Sandhya; Hampson, Johnson P; Hampson, Jaison; Hupp, Norma J; Sainju, Rup K; Friedman, Daniel; nei, maromi; Scott, Catherine; Allen, Luke; Gehlbach, Brian K; Schuele, Stephan; Harper, Ronald M; Diehl, Beate; Bateman, Lisa M; Devinsky, Orrin; Richerson, George B; Zhang, Guo-Qiang; Lhatoo, Samden D; and Lacuey, Nuria, "Automated Analysis of Risk Factors for Postictal Generalized EEG Suppression" (2021). Department of Neurology Faculty Papers. Paper 250.
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