Udaya Seneviratne1, Ray C Boston2, Mark Cook3, Wendyl D'Souza4. 1. Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Australia; Department of Neuroscience, Monash Medical Centre, Melbourne, Australia; School of Clinical Sciences at Monash Health, Department of Medicine, Monash University, Melbourne, Australia. Electronic address: Udaya.Seneviratne@svhm.org.au. 2. Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Australia. Electronic address: drrayboston@yahoo.com. 3. Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Australia. Electronic address: markcook@unimelb.edu.au. 4. Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Australia. Electronic address: wendyl@unimelb.edu.au.
Abstract
OBJECTIVE: We sought to investigate the temporal patterns and sleep-wake cycle-related epileptiform discharges (EDs) in genetic generalized epilepsies (GGEs). METHODS: We studied 24-hour ambulatory electroencephalography (EEG) recordings of patients with GGE, diagnosed and classified according to the International League against Epilepsy criteria. We manually coded the type of discharge, time of occurrence, duration, and arousal state of each ED. We employed mixed effects Poisson regression modeling to study the temporal distribution of epileptiform discharges. Additionally, we used multinomial regression analysis to explore the significance of the relationship between different states of arousal and types of epileptiform discharges. RESULTS: We analyzed 6923 EDs from 105 abnormal 24-hour EEGs. Mixed effects Poisson regression analysis demonstrated significant changes in ED counts across time blocks. This distribution was largely influenced by the state of arousal. Generalized fragments (duration<2s) and focal discharges were more frequent during non-REM sleep while paroxysms (duration≥2s) were more frequent in wakefulness. Overall, 67% of epileptiform discharges occurred in non-REM sleep and only 33% occurred in wakefulness. Twenty-four patients (23%) had ED exclusively in sleep. Epileptiform discharges peaked from 23:00 through 07:00h. SIGNIFICANCE: There is a time-of-day dependency of ED with a significant influence exerted by the state of arousal. Our observations suggest that the generation of epileptiform discharges is not a random process but is the result of complex interactions among biological rhythms such as the sleep-wake cycle and the intrinsic circadian pacemaker. High density of ED in sleep suggests that 24-hour EEG recording with the capture of natural sleep may be more useful than routine EEG to diagnose GGE.
OBJECTIVE: We sought to investigate the temporal patterns and sleep-wake cycle-related epileptiform discharges (EDs) in genetic generalized epilepsies (GGEs). METHODS: We studied 24-hour ambulatory electroencephalography (EEG) recordings of patients with GGE, diagnosed and classified according to the International League against Epilepsy criteria. We manually coded the type of discharge, time of occurrence, duration, and arousal state of each ED. We employed mixed effects Poisson regression modeling to study the temporal distribution of epileptiform discharges. Additionally, we used multinomial regression analysis to explore the significance of the relationship between different states of arousal and types of epileptiform discharges. RESULTS: We analyzed 6923 EDs from 105 abnormal 24-hour EEGs. Mixed effects Poisson regression analysis demonstrated significant changes in ED counts across time blocks. This distribution was largely influenced by the state of arousal. Generalized fragments (duration<2s) and focal discharges were more frequent during non-REM sleep while paroxysms (duration≥2s) were more frequent in wakefulness. Overall, 67% of epileptiform discharges occurred in non-REM sleep and only 33% occurred in wakefulness. Twenty-four patients (23%) had ED exclusively in sleep. Epileptiform discharges peaked from 23:00 through 07:00h. SIGNIFICANCE: There is a time-of-day dependency of ED with a significant influence exerted by the state of arousal. Our observations suggest that the generation of epileptiform discharges is not a random process but is the result of complex interactions among biological rhythms such as the sleep-wake cycle and the intrinsic circadian pacemaker. High density of ED in sleep suggests that 24-hour EEG recording with the capture of natural sleep may be more useful than routine EEG to diagnose GGE.
Authors: Philippa J Karoly; Dean R Freestone; Dominique Eden; Rachel E Stirling; Lyra Li; Pedro F Vianna; Matias I Maturana; Wendyl J D'Souza; Mark J Cook; Mark P Richardson; Benjamin H Brinkmann; Ewan S Nurse Journal: Front Neurol Date: 2021-08-04 Impact factor: 4.003
Authors: Victor Ferastraoaru; Daniel M Goldenholz; Sharon Chiang; Robert Moss; William H Theodore; Sheryl R Haut Journal: Epilepsia Open Date: 2018-07-04