Temporal patterns of epileptiform discharges in genetic generalized epilepsies Udaya Seneviratne a,b,c, ⁎, Ray C. Boston a , Mark Cook a , Wendyl D'Souza a a Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Australia b Department of Neuroscience, Monash Medical Centre, Melbourne, Australia c School of Clinical Sciences at Monash Health, Department of Medicine, Monash University, Melbourne, Australia abstract article info Article history: Received 23 June 2016 Revised 9 September 2016 Accepted 10 September 2016 Available online xxxx 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 b 2 s) and focal discharges were more frequent during non-REM sleep while paroxysms (duration ≥ 2 s) were more frequent in wakefulness. Overall, 67% of epilepti- form 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 07:00 h. 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. © 2016 Elsevier Inc. All rights reserved. Keywords: EEG Sleep Circadian Spike–wave Generalized epilepsy 1. Introduction Temporal patterns in the occurrence of epileptic seizures have been described by researchers for several decades [1–3]. Only a few studies have investigated temporal patterns of epileptiform discharges [4–7]. Even those studies were based on relatively small numbers of patients (n = 19, 17, 5, and 5, respectively) from cohorts with mixed focal and generalized epilepsy. The close relationship between the sleep–wake cycle and epileptiform discharges (EDs) has been highlighted [8]. Generalized spike–wave dis- charges are more frequent during nonrapid eye movement (NREM) sleep than in wakefulness and least common during REM sleep [9]. These studies suggest that circadian rhythms are relevant to epileptogenicity and highlight the influence of sleep–wake cycle on epileptiform discharges. However, it is difficult to draw robust conclu- sions because of methodological problems such as small sample size and the lack of a uniform protocol for EEG recording. Against this backdrop, we sought to investigate two research ques- tions in relation to genetic generalized epilepsy (GGE): (1) Is there a tem- poral pattern (time-of-day dependency) in the occurrence of ED? (2) Is there a difference in ED quantity between sleep and wakefulness? To ex- plore these questions, we conducted the current study based on 24-hour ambulatory EEG recordings in a well-characterized cohort of patients diagnosed with GGE. We also sought to assess the diagnostic yield of EEG based on temporal patterns. We hypothesized that epileptiform discharges follow an intrinsic rhythm influenced by the sleep–wake cycle. 2. Materials and methods 2.1. Case ascertainment The methodology of our research has been previously described [10,11]. In summary, we prospectively recruited patients through Epilepsy & Behavior 64 (2016) 18–25 ⁎ Corresponding author at: Department of Neuroscience, St. Vincent's Hospital, PO Box 2900, Fitzroy, VIC 3065, Melbourne, Australia. E-mail addresses: Udaya.Seneviratne@svhm.org.au (U. Seneviratne), drrayboston@yahoo.com (R.C. Boston), markcook@unimelb.edu.au (M. Cook), wendyl@unimelb.edu.au (W. D'Souza). http://dx.doi.org/10.1016/j.yebeh.2016.09.018 1525-5050/© 2016 Elsevier Inc. All rights reserved. Contents lists available at ScienceDirect Epilepsy & Behavior journal homepage: www.elsevier.com/locate/yebeh