BACKGROUND: There is no standardized EEG duration guideline for detecting epileptiform abnormalities in patients, and research on this topic is scarce. This study aims to determine an optimal EEG duration for efficient detection of epileptiform abnormalities across different patient groups.
METHODS: Retrospective analysis was performed on EEG recordings and clinical data of patients with the first seizure and epilepsy. Patients were categorized based on various factors, including the interval time since the last seizure, use of anti-seizure medication (ASM), and seizure frequency. The detection ratio (DR) of epileptiform abnormalities and latency time for their discovery were calculated. Statistical analyses, including chi-square tests, logistic regression, and survival analysis were utilized to illustrate DR and latency times.
RESULTS: In whole-night EEG recordings, the DR was 37.6% for the first seizure group and 57.4% for the epilepsy group. Although the maximum latency times were 720 min in both two groups, DR in the first seizure group was distinctly decreased beyond 300 min. Significant factors influencing the DR included the use of ASM in the first seizure group (P < 0.05) and seizure frequency in the epilepsy group (P < 0.001). For epilepsy patients who experience a seizure at least once a month or undergo timely EEG recordings (within 24 h after a seizure), the DR significantly increases, and the maximum latency time is reduced to 600 min (P < 0.001). Additionally, the DR was significantly reduced after 240 min in epilepsy patients who had been seizure-free for more than one year.
CONCLUSIONS: In this retrospective study, we observed a maximum latency of 720 min for detecting epileptiform abnormalities in whole-night EEG recordings. Notably, epilepsy patients with a higher seizure frequency or timely EEG recordings demonstrated both a higher detection ratio and a shorter maximum latency time. For patients exhibiting a low detection ratio, such as those experiencing their first seizure or those with epilepsy who have been seizure-free for more than a year, a shorter EEG duration is recommended. These findings underscore the importance of implementing customized EEG strategies to meet the specific needs of different patient groups.

Abnormal patterns identified on electroencephalogram (EEG) are one of the primary diagnostic tests for epilepsy. However, epidemiological studies have established that both benign and epileptiform abnormalities (EAs) occur on the EEG of nonepileptic, seizure-free people as well. The reported rates of EAs in nonepileptic, seizure-free populations vary, and the true prevalence is unknown. The primary objective of this systematic review and meta-analysis was to estimate the overall prevalence of EAs in the EEG of people without a history of seizures. Secondary aims were to characterize (1) the cortical localization of focal abnormalities, (2) the proportion of findings that occurred during standard EEG stimulation protocols, and (3) the persistence and implications of abnormalities at follow-up. A comprehensive electronic search of six bibliographic databases was completed: Embase, MEDLINE, PsycInfo, Cumulative Index of Nursing and Allied Health Literature, Cochrane Central Register for Controlled Trials, and Web of Science. No search date restrictions were applied. Overall effect size was calculated using a generalized linear mixed-effects model. Fifty-three studies, totaling 73 990 individuals, met our inclusion criteria. The overall point prevalence of EAs was 1.74% (95% confidence interval [CI] = 1.13-2.67). Due to the risk of bias in the literature, especially from participant selection, we believe this to be an overestimate of the true prevalence. Prevalence of EAs was greater in children (2.45%, 95% CI = 1.41-4.21) and the elderly (5.96%, 95% CI = 1.39-22.13) compared with adults (.93%, 95% CI = .48-1.80). Reports of developing epilepsy after an EA-positive EEG were rare. The likelihood of subsequent positive findings on follow-up EEG may be as high as 50%. Our study has limitations in that males were overrepresented in the study samples, there is substantial heterogeneity among studies, and many studies provided insufficient detail about their exclusion criteria. Nonetheless, our estimates provide benchmark data for future studies examining EAs in clinical populations, particularly behavioral and psychiatric populations.

BACKGROUND: The yield of epileptiform EEG abnormalities is lower in unselected Paediatric populations than in prospective studies of incident seizures or prevalent epilepsy studies. At a time of limited capacity, it is important to match available EEG resources to children who are most likely to benefit. In this study we evaluated a prospective triage tool for estimating the likelihood of epileptiform abnormality in children\'s first out-patient EEG.
METHODS: We prospectively triaged 1865 out-patient referrals to the largest Paediatric EEG laboratory in Ireland. Based on a structured algorithm, we dichotomized first EEG referrals into priority and non-priority groups and assigned one of 5 sub-levels based on anticipated EEG yield. EEGs were reported by a single Consultant in Clinical Neurophysiology.
RESULTS: Triage designated 757 (41 %) EEG referrals as non-priority. Priority exceeded non-priority referrals for all age groups except children between 18 months and 3.5 years. EEGs showed a two-fold higher incidence of interictal epileptiform abnormalities for priority referrals (36 % vs 18 %, p < 0.001). Rates of interictal epileptiform abnormality correlated with the 5 sub-levels of triage (p < 0.01). Epileptiform yield was highest (39 %) for children over 5 years vs 17 % for those under 5 years (p < 0.00001); these rates increased to 49 % and 20 % respectively for priority referrals.
CONCLUSIONS: Structured pre-test triage of EEG referrals can identify children who have the greatest likelihood of epileptiform abnormality. In a mixed population of Paediatric referrals, the epileptiform yield of first time EEG is 49 % for children over 5 years who are referred with an appropriate EEG indication. This is subject to much variability with epileptiform yields as low as 13 % in younger children with non-priority referrals. The use of a structured triage algorithm can help to optimise utility of EEG in situations of limited laboratory capacity.

OBJECTIVE: The yield of epileptiform abnormalities in serial electroencephalography (EEG) studies has not been addressed in a population-based setting for subjects with incident epilepsy or a single unprovoked seizure, raising the possibility of methodologic limitations such as selection bias. Our aim was to address these limitations by assessing the yield and predictors of epileptiform abnormalities for the first and subsequent EEG recording in a study of incident epilepsy or single unprovoked seizure in Rochester, Minnesota.
METHODS: We used the resources of the Rochester Epidemiology Project to identify all 619 residents of Rochester, Minnesota, born in 1920 or later with a diagnosis of incident epilepsy (n = 478) or single unprovoked seizure (n = 141) between 1960 and 1994, who had at least one EEG study. Information on all EEG studies and their results was obtained by comprehensive review of medical records.
RESULTS: Among subjects with epilepsy, the cumulative yield of epileptiform abnormalities was 53% after the first EEG study and 72% after the third. Among subjects with a single unprovoked seizure, the cumulative yield was 39% after the first EEG study and 68% after the third. Young age at diagnosis and idiopathic etiology were risk factors for finding epileptiform abnormalities across all EEG recordings.
CONCLUSIONS: Although the cumulative yield of epileptiform abnormalities increases over successive EEG recordings, there is a decrease in the increment for each additional EEG study after the first EEG study. This is most evident in incident epilepsy and in younger subjects. Clinically it may be worthwhile to consider that the probability of finding an epileptiform abnormality after the third nonepileptiform EEG recording is low.