Diagnostic Yield of Routine EEG with Video: A Retrospective Analysis of Its Clinical Utility in Adults at a Tertiary Care Center

Abstract

Background

Routine electroencephalography (EEG) with video (rVEEG) is a widely used, noninvasive neurodiagnostic tool that aids in the diagnosis and classification of epilepsy by detecting interictal epileptiform discharges. This study aimed to assess its diagnostic utility and identify key clinical correlates in adult patients at a tertiary care center in North India, where specific data on its yield in this resource-constrained context are valuable.

Materials and Methods

We retrospectively analyzed routine EEGs with simultaneous video recording performed in adults aged ≥18 years. Clinical and demographic data—including seizure type, age at onset, etiology, antiepileptic drug use, seizure timing, and activation procedures—were extracted from EEG records and electronic medical records. EEGs were categorized as normal or abnormal, with abnormalities classified as epileptiform or nonepileptiform.

Results

Among 347 patients (mean age 41.6 ± 17.8 years), most were aged 18 to 30 (58.2%), with 41.4% experiencing seizure onset before the age of 18 years. Focal seizures were predominant (76.3%), especially focal impaired awareness seizures (52.7%). Etiology was unclear in 85.3% of cases. Neuroimaging data (computed tomography or magnetic resonance imaging) were available for 74 patients; 40 had normal findings, and 34 showed lesions. rVEEGs were commonly performed within 1 week of the last seizure (83%), often during sleep (57.3%). EEG abnormalities were present in 37.5%, primarily generalized spike-and-wave discharges (25.6%). Most patients were on monotherapy (86.2%). Video recording led to a revision or refinement of the initial clinical diagnosis in 7.5% (26) of patients. No significant clinical predictors of abnormal EEG findings were identified (p > 0.05).

Conclusion

The diagnostic yield of rVEEG in this cohort was modest. Optimizing patient selection based on detailed clinical history may improve EEG utility in resource-constrained settings.

Keywords

routine EEG with video - epilepsy - diagnostic yield - seizures - resource-limited settings

Introduction

Routine electroencephalography (EEG) with video (rVEEG) is a widely used, noninvasive, and cost-effective neurodiagnostic tool. Typically performed over 20 to 30 minutes, it is useful for detecting interictal epileptiform discharges (IEDs), which aid in the diagnosis and classification of epilepsy.[1] Long-term video EEG (vEEG), conducted over several days in an epilepsy monitoring unit, remains the gold standard for evaluating drug-resistant epilepsy and capturing ictal events.[2] However, such continuous monitoring requires specialized infrastructure and trained personnel, making it challenging to implement in low-resource settings.[3]

rVEEG offers a more accessible alternative in such environments. Although limited in duration, prior studies have demonstrated its utility in capturing clinically meaningful data, distinguishing epileptic from nonepileptic events, and aiding seizure classification.[4] [5] It is also frequently used to evaluate altered mental status, behavioral disturbances, and postcardiac arrest prognostication.[6] However, the diagnostic yield of routine EEG varies widely, with sensitivity for detecting IEDs reported between 25 and 56%.[7] Diagnostic performance is particularly limited in adults and patients with focal epilepsy, and there remains a risk of overinterpretation or false-positive findings in the absence of clinical correlation.[8]

Despite its limitations, routine EEG continues to play a critical role in epilepsy evaluation, especially where access to long-term monitoring and advanced imaging is limited. While studies on EEG utility have largely originated from high-resource settings, comprehensive data from low- and middle-income countries are valuable, as health care infrastructure, referral patterns, and patient demographics can differ significantly.[9]

This study was conducted in a resource-constrained tertiary care center in North India, where access to long-term vEEG and magnetic resonance imaging (MRI) is often limited. We aimed to evaluate the diagnostic yield of rVEEG in this specific setting and to explore whether clinical variables could predict abnormal EEG findings. By analyzing real-world data from a diverse patient population, this study contributes practical insights into optimizing EEG use in similar low-resource environments.

Materials and Methods

This retrospective observational study was conducted in the Department of Neurology at All India Institutes of Medical Sciences (AIIMS), Raebareli, a tertiary care academic hospital in northern India. The study included adult patients (aged 18 years and older) who underwent rVEEG between November 2023 and January 2025. Ethical clearance was obtained from the Institutional Ethics Committee prior to data collection. Both inpatients and outpatients referred for EEG during this period were considered for inclusion. Patients of either gender with clinical suspicion of seizure disorders, including epileptic seizures, psychogenic nonepileptic seizures (PNES), or other paroxysmal events, were eligible. Patients were excluded if they were younger than 18 years of age, had poor-quality EEG recordings, were medically unstable during testing, or had incomplete clinical records.

Clinical and demographic data were retrospectively obtained from EEG registers and hospital medical records. Variables collected included age, gender, clinical diagnosis at the time of referral, seizure type (classified according to the 2017 International League Against Epilepsy [ILAE] criteria),[10] number of antiseizure medications (ASMs) prescribed at the time of the test, the reason for EEG referral, imaging findings if available (computed tomography [CT]/MRI), and final EEG findings. Clinical events captured during the recording were also documented, including motor manifestations, awareness level, responsiveness, and postictal behaviors.

All EEGs were performed in a dedicated neurophysiology laboratory using the NIHON KODEN V32 digital EEG system. A standard 32-channel configuration was used, and electrodes were applied according to the international 10–20 electrode placement system. Bipolar, referential, and average montages were used during interpretation. Each EEG session lasted approximately 30 minutes and included simultaneous video recording to facilitate clinical correlation. Patients were positioned in a semirecumbent posture in a quiet, dimly lit room and were asked to keep their eyes gently closed unless otherwise instructed. Activation procedures included eye opening and closing, hyperventilation (HV) for 3 minutes when feasible, and intermittent photic stimulation (IPS) using a strobe lamp flashing at frequencies ranging from 1 to 35 Hz. Sleep deprivation was suggested for some patients, but was not a routine requirement. No sedatives were administered before or during the recording, although spontaneous sleep was observed in several patients.

All EEGs were initially screened by a certified EEG technologist (ABRET R.EEG.T) to ensure technical quality and identify notable features. Subsequently, a neurology resident with EEG training interpreted the findings, and final approval was given by a consultant neurologist with expertise in epilepsy. EEGs were categorized into one of five diagnostic groups: normal EEG (no abnormalities), focal IEDs (sharp waves or spikes localized to one region or hemisphere), generalized IEDs (bilaterally synchronous spike-and-wave or polyspike discharges), focal slowing (suggestive of localized cerebral dysfunction), and generalized slowing (indicating diffuse encephalopathy). Benign variants such as small sharp spikes, 14- and 6-Hz positive bursts, wicket spikes, and phantom spikes were excluded from the IED category.

Brain imaging reports (CT or MRI) were reviewed when available. The availability of neuroimaging data was specifically noted for each patient, and patients were categorized as having normal imaging, structural abnormalities (e.g., gliosis, atrophy, malformations), or no available imaging data. Etiological classification of epilepsy was based on ILAE guidelines and included structural, infectious, genetic, metabolic, immune, and unknown categories.

Statistical Analysis

Data were compiled in Microsoft Excel and analyzed using SPSS version 27 (IBM Corp., Armonk, New York, United States). Continuous variables were reported as means with standard deviations or medians with interquartile ranges, depending on distribution. Categorical data were presented as frequencies and percentages. Comparisons between groups were made using Student's t-test or Mann–Whitney U-test for continuous variables and chi-square or Fisher's exact test for categorical variables. To identify factors associated with abnormal EEG findings, univariate logistic regression analysis was conducted. A p-value of less than 0.05 was considered statistically significant.

Results

This retrospective study included a total of 347 adult patients who underwent rVEEG. The majority of patients were male (n = 187, 53.8%), and the most represented age group was 18 to 30 years (n = 202, 58.2%) ([Table 1]). A younger age of seizure onset (<18 years) was seen in 144 patients (41.4%), and most participants had no family history of seizures (99.1%) ([Table 2]).

Table 1 Demographic characteristics of the study participants

Variables		Frequency, n (%)
Age (y)	18–30	202 (58.2)
	31–40	68 (19.6)
	41–50	30 (8.6)
	51–60	10 (2.9)
	>60	37 (10.7)
Gender	Male	187 (53.8)
	Female	160 (46.2)

Table 2 Details on history of seizure

Variables			Frequency, n (%)
Age at onset of first seizure (y)		<18	144 (41.4)
		18–30	124 (35.7)
		31–40	45 (13.0)
		41–50	15 (4.3)
		51–60	9 (2.6)
		>60	10 (2.9)
Family history of seizure		Yes	3 (0.9)
		No	344 (99.1)
Types of seizure	Generalized seizures	Tonic–clonic seizures (Grand Mal)	33 (9.5)
		Absence seizures (Petit Mal)	6 (1.7)
	Focal seizures	Focal aware seizures	18 (5.2)
		Focal impaired awareness seizure	183 (52.7)
		Focal with bilateral convulsive seizures	64 (18.4)
	Unknown		43 (12.4)
Etiology of seizure	Structural		8 (2.3)
	Infections		31 (8.9)
	Vascular		12 (3.5)
	Unknown		296 (85.3)

The most frequent clinical seizure type was focal impaired awareness seizures, documented in 183 individuals (52.7%), followed by focal to bilateral tonic–clonic seizures (n = 64, 18.4%). Other less common types included focal aware seizures, generalized tonic–clonic, and absence seizures. In 12.4% of cases, the seizure type could not be classified due to limited clinical information ([Table 2]).

Etiologically, the underlying cause of seizures remained unknown in 85.3% (n = 296) of the study population. Among identifiable etiologies, infections such as neurocysticercosis and tuberculoma accounted for 31 cases (8.9%), while structural and vascular causes were seen in 2.3 and 3.5% of patients, respectively. Regarding neuroimaging availability, data from CT or MRI scans were available for review in 74 patients. Of these 74 patients, 40 had within normal limits or normal imaging findings, and 34 had other lesions or abnormality findings. Imaging data were not recorded or unavailable for the remaining 273 patients.

With regard to treatment, most patients (n = 299, 86.2%) were on monotherapy with ASMs, while 37 patients (10.7%) were on dual therapy. Only a few required three or more ASMs or were not on any treatment at all ([Table 3]).

Table 3 Details on ASMs

Numberof ASM	Frequency, n (%)
None	3 (0.9)
One	299 (86.2)
Two	37 (10.7)
Three	5 (1.4)
Four	1 (0.3)
Not applicable	2 (0.6)

Abbreviation: ASM, antiseizure medication.

The primary indication for EEG referral was the evaluation of suspected epileptic seizures (n = 288, 83.0%). Other reasons included episodes of loss of consciousness (6.1%), abnormal movements (5.5%), stroke (2.6%), and nonepileptic causes such as visual hallucinations or presurgical evaluation ([Table 4]). EEG was recorded within 1 week of the most recent seizure in 283 patients (81.6%). A drowsy or sleep state was achieved in 57.3% of the recordings, which likely increased diagnostic yield. A representative EEG showing normal posterior dominant alpha rhythm with appropriate reactivity to eye opening and closure is provided in [Fig. 1].

Fig. 1 A 10-second EEG recording from a 12-year-old awake male, demonstrating a posterior dominant alpha rhythm during eye closure. The alpha activity attenuates with eye opening and reappears with eye closure, indicating normal alpha reactivity, which reflects intact posterior cortical function. EEG, electroencephalography.

Table 4 Details on EEG

Variables			Frequency, n (%)
Reason for EEG requisition	Seizures		288 (83.0)
	Headache		5 (1.4)
	Stroke		9 (2.6)
	Loss of consciousness		21 (6.1)
	Abnormal body movements		19 (5.5)
	Plan to stop ASM		1 (0.3)
	Surgery		2 (0.6)
	Visual hallucination		1 (0.3)
	DRE		1 (0.3)
Time between the EEG and the last episode of seizure	Less than a week		283 (81.6)
	Less than a month		22 (6.3)
	Less than 6 months		28 (81.)
	Less than a year		8 (2.3)
	More than a year		6 (1.7)
State during recording	Drowsy (A + D)		148 (42.7)
	Sleep (A + D + S)		199 (57.3)
Recent EEG changes	Normal		217 (62.5)
	Abnormal	Spike and wave	89 (25.6)
		Sharp wave	30 (8.6)
		Slow wave	11 (3.2)

Abbreviations: ASM, antiseizure medication; DRE, drug-resistant epilepsy; EEG, electroencephalography.

Out of 347 EEGs reviewed, 130 (37.5%) showed abnormalities ([Table 4]). The most common EEG abnormalities included spike-and-wave discharges (n = 89, 25.6%), followed by sharp waves (n = 30, 8.6%) and slow waves (n = 11, 3.2%). The remainder (62.5%) had normal EEGs. Based on standardized EEG classification, focal IEDs were observed in 59 patients (17.0%) ([Fig. 2]) and generalized IEDs in 30 patients (8.6%) ([Fig. 3]). Focal slowing was noted in 22 patients (6.3%) and generalized slowing in 11 patients (3.2%) ([Fig. 4]).

Fig. 2 Routine EEG of a 20-year-old woman demonstrating a normal background rhythm with generalized spike-and-wave discharges predominantly over the frontal regions. The corresponding MRI was reported as normal, with no structural abnormalities detected. EEG, electroencephalography.

Fig. 3 A 22-year-old man presented with right temporal gliosis on neuroimaging. EEG revealed intermittent spike-and-wave discharges localized to the right temporal region, consistent with a focal epileptiform pattern. EEG, electroencephalography.

Fig. 4 A 58-year-old man presented with acute altered sensorium and confusion. His medical history was unknown, and there were no signs of seizure activity. EEG revealed intermittent, generalized delta slowing, suggestive of a diffuse cerebral dysfunction. EEG, electroencephalography.

The simultaneous video recording proved to be a valuable component, leading to a revision or refinement of the initial clinical diagnosis in 7.5%[11] of patients. These revisions primarily involved distinguishing misclassified seizures, identifying PNES, or recognizing other paroxysmal nonepileptic events such as movement disorders. For patients diagnosed with PNES based on video correlation, this often led to important changes in management, including the avoidance or discontinuation of unnecessary ASMs.

To identify clinical predictors of abnormal EEG findings, a binary logistic regression analysis was conducted ([Table 5]). However, no statistically significant associations were found between EEG abnormalities and variables such as age group, gender, age at seizure onset, seizure type, etiology, or time between the last seizure and EEG recording (p > 0.05 for all comparisons). This lack of correlation may reflect the heterogeneity of the study population and limitations in seizure classification due to retrospective data collection.

Table 5 Logistic regression to evaluate the predictors of EEG

Variables			EEG changes		UOR	p-Value	AOR	p-Value
			Normal	Abnormal
Age (y)	18–30		128 (59.0)	69 (58.0)	1.032 (0.871–1.223)	0.712	1.049 (0.862–1.276)	0.634
	31–40		44 (20.3)	21 (17.6)
	41–50		15 (6.9)	14 (11.8)
	51–60		9 (4.1)	1 (0.8)
	>60s		21 (9.7)	14 (11.8)
Gender	Male		118 (54.4)	63 (52.9)	1.070 (0.683–1.676)	0.767	1.103 (0.694–1.752)	0.678
	Female		99 (45.6)	56 (47.1)	1	–	–	–
Age at onset of first seizure (y)	<18		93 (42.9)	46 (38.7)	0.935 (0.827–1.056)	0.280	1.004 (0.799–1.261)	0.972
	18–30		73 (33.6)	49 (41.2)
	31–40		30 (13.8)	13 (10.9)
	41–50		8 (3.7)	6 (5.0)
	51–60		7 (3.2)	2 (1.7)
	>60		6 (2.8)	3 (2.5)
Types of seizure	Generalized seizures	Tonic–clonic seizures (Grand Mal)	20 (9.2)	13 (10.9)	1.400 (0.536–3.654)	0.492	1.508 (0.567–4.016)	0.411
		Absence seizures (Petit Mal)	4 (1.8)	2 (1.7)	1.077 (0.174–6.649)	0.936	0.989 (0.157–6.216)	0.991
	Focal seizures	Focal aware seizures	12 (5.5)	6 (5.0)	1.077 (0.331–3.506)	0.902	1.113 (0.334–3.707)	0.861
		Focal impaired awareness seizure	119 (54.8)	59 (49.6)	1.068 (0.516–2.212)	0.860	1.033 (0.491–2.174)	0.932
		Focal with bilateral convulsive seizures	34 (15.7)	26 (21.8)	1.647 (0.716–3.788)	0.240	1.581 (0.676–3.696)	0.291
	Unknown		28 (12.9)	13 (10.9)	1	–	–	–
Etiology of seizure	Structural		5 (2.3)	2 (1.7)	0.723 (0.138–3.792)	0.701	1.064 (0.185–6.107)	0.945
	Infections		18 (8.3)	12 (10.1)	1.205 (0.559–2.599)	0.634	1.241 (0.561–2.742)	0.594
	Vascular		6 (2.8)	1 (0.8)	0.301 (0.36–2.537)	0.270	0.282 (0.032–2.521)	0.258
	Unknown		188 (86.8)	104 (87.4)	1	–	1	–
Time between EEG and last episode of seizure	Less than a week		174 (80.2)	103 (86.6)	0.812 (0.609–1.082)	0.155	0.793 (0.587–1.071)	0.130
	Less than a month		15 (6.9)	5 (4.2)
	Less than 6 months		17 (7.8)	8 (6.7)
	Less than a year		6 (2.8)	2 (1.7)
	More than a year		5 (2.3)	1 (0.8)

Abbreviations: EEG, electroencephalography; UOR, unadjusted odds ratio; AOR, adjusted odds ratio.

Discussion

rVEEG remains a cornerstone diagnostic modality in the evaluation and management of adult epilepsy. It is instrumental in identifying IEDs, classifying seizure types, and distinguishing epileptic seizures from mimics such as PNES.[4] Our study assessed the diagnostic yield and referral patterns of rVEEG in a real-world, resource-constrained tertiary care setting. Only 37.5% of EEGs were abnormal, with 34.2% showing epileptiform discharges. While a normal EEG does not rule out epilepsy, this rate is comparable to diagnostic yields reported in various literature for initial EEG recordings.

Our study revealed a high proportion (85.3%) of unknown etiology for seizures, significantly higher than rates in developed countries where structural or genetic causes are more frequently identified. This disparity directly reflects diagnostic challenges in resource-constrained settings, primarily due to limited neuroimaging access. Out of 347 patients, only 74 had neuroimaging data available (40 normal, 34 with lesions), with 273 lacking recorded imaging. This limited access to advanced modalities such as high-resolution MRI often precludes definitive identification of subtle structural abnormalities. For instance, in contrast to some studies from India that report 25 to 35% structural etiology, our findings underscore the critical need for improved access to comprehensive diagnostic tools to better classify etiologies in such regions.[12] [13]

Most EEGs in our study were ordered specifically to detect epileptiform activity. However, it is well established that IEDs may not appear on a single routine EEG, even in patients with confirmed epilepsy. McGinty et al. emphasized the value of video EEG in distinguishing PNES from epileptic seizures.[14] Similarly, our study found that approximately 20% of referrals were for suspected PNES,[15] highlighting rVEEG's utility as a screening tool in outpatient settings where long-term monitoring is not feasible. This utility was further demonstrated by the fact that video correlation facilitated a revision or refinement of the initial clinical diagnosis in 7.5%[11] of patients, supporting its role in differentiating seizure types during brief recordings. This observation aligns with previous Indian studies by Srikumar et al and Nada et al, which also demonstrated the utility of rVEEG in differentiating seizure types during brief recordings.[16] [17]

In our study, the majority of EEGs (81.6%) were performed within a week of the most recent seizure occurrence. Despite the timely scheduling, our multivariable logistic regression analysis did not identify a statistically significant association between the time since the last seizure and the likelihood of receiving aberrant EEG data. This is in contrast with earlier research. For example, a first seizure cohort study discovered that EEGs performed within 24 hours of the incident revealed epileptiform discharges in 51% of cases, compared to 34% when performed beyond 24 hours.[14] [18] Another study involving 203 individuals with a history of seizures found that 77% of EEGs were positive for epileptiform discharges when taken within 2 days; however, only 41% would be positive if the seizure had occurred 7 or more days prior.[19] These variations could be due to variances in methodology, seizure categorization accuracy, or demographic variables.[20]

All common activation procedures—sleep, HV, and IPS—were utilized in our recordings. Among these, sleep activation had the highest yield, with IEDs seen in 57.3% of patients. This supports prior findings that sleep enhances EEG sensitivity.[21] [22] However, HV and IPS were less effective, consistent with previous studies reporting yields of only 5.0 and 3.3%, respectively.[17] Still, both remain valuable for identifying specific epilepsy syndromes, such as photosensitive epilepsy.

Most patients in our cohort (86.2%) were on monotherapy, with 12.4% on two or more ASMs. We found no significant correlation between the number of ASMs and EEG abnormality, unlike pediatric studies, where higher ASM usage correlated with increased epileptiform activity.[23] [24] This discrepancy could be due to differences in epilepsy type, disease duration, and medication response. Notably, Guida et al reported that increasing ASMs in adults with refractory epilepsy did not consistently reduce epileptiform activity.[23] Our findings support the notion that EEG abnormalities are not solely determined by the number of ASMs and emphasize the need for tailored management and follow-up strategies.

The timing of EEG recording in relation to seizure onset is critical for IED detection.[25] Pohlmann-Eden and Newton suggested a transient hyperexcitable state following seizures, increasing the likelihood of detecting abnormalities on EEG recorded soon after an event.[11] While early EEGs (within 24 hours) show higher yields, our study did not find a statistically significant difference, possibly due to retrospective design or sample variability.[26] Nevertheless, among patients scanned within a week of seizure onset, 86.6% of abnormal EEGs were found in this subgroup.

A subset of patients (2.6%) were stroke survivors. While none exhibited epileptiform discharges, diffuse slowing was observed, likely indicating encephalopathy rather than active epileptogenesis.[27] This aligns with previous findings by Macdonell et al, who noted that routine EEG in poststroke settings has limited value unless nonconvulsive seizures or altered mental status are suspected.[28] In such cases, long-term EEG monitoring is preferred for higher sensitivity.

In comparison, a study by Tayeb et al from Saudi Arabia reported a 42.4% EEG abnormality rate, with 14.6% of patients exhibiting epileptiform discharges. They found a significant association between EEG abnormalities and a history of stroke, as well as the use of multiple ASMs. In contrast, our findings emphasize the added diagnostic utility of rVEEG correlation, which enhances the ability to distinguish epileptic from nonepileptic events even in brief recordings. This makes it a cost-effective and practical tool, particularly well suited for outpatient settings.[29]

Our cohort encompassed various seizure types, with focal impaired awareness seizures (52.7%) being the most common, followed by focal to bilateral tonic–clonic seizures (18.4%), focal aware seizures (5.2%), generalized tonic–clonic seizures (9.5%), and absence seizures (1.7%). Interestingly, although focal seizures dominated clinically, the most frequent EEG abnormality was generalized spike-and-wave discharges. This clinical–electrophysiological mismatch could be due to limitations in seizure classification in retrospective reviews, which rely on clinical notes rather than direct observation. It underscores the diagnostic contribution of EEG in refining seizure classification, especially in resource-limited contexts. Moreover, the predominance of focal epilepsies, which tend to have a lower EEG yield than idiopathic generalized epilepsies (IGEs), may partly explain our modest rate of IED detection. Myers et al found that IGEs were strong predictors of IED presence on routine EEG, likely due to the higher rates of myoclonic and absence seizures in younger individuals.[30] [31]

Strengths and Limitations

This study's strengths include a relatively large adult sample from a tertiary care hospital in a resource-limited setting, enhancing its relevance to similar clinical environments. rVEEG enabled electroclinical correlation, aiding in the identification of PNES and other nonepileptic events. Standardized EEG protocols and accepted classification methods improved internal validity and allowed meaningful subgroup analysis. The diverse patient population also supports generalizability.

However, as a retrospective study, it is limited by incomplete data and potential selection bias. The short EEG duration (20–30 minutes) may have missed intermittent or habitual events, and repeat EEGs were often not feasible. Neuroimaging was unavailable in over half the cases, contributing to the high rate of unknown etiology. While video captured clinical events in a subset of patients, and when present, significantly aided diagnostic refinement, particularly in differentiating epileptic from nonepileptic events. Lack of follow-up prevented outcome assessment, and some subgroup analyses were underpowered.

Conclusion

In summary, rVEEG in adult patients demonstrates a modest yet clinically meaningful diagnostic yield, particularly in resource-constrained settings. The addition of video significantly improved diagnostic accuracy; notably, in 7.5% (26 patients), the diagnosis was either revised or refined based on video correlation (e.g., distinguishing misclassified seizures, PNES, or movement disorders). While no consistent clinical predictors of abnormal EEG findings emerged, rVEEG remains a valuable tool for initial evaluation and classification of seizure disorders. Optimizing patient selection and incorporating activation procedures can further enhance its utility, ensuring appropriate and efficient use of neurodiagnostic resources.

Conflict of Interest

None declared.

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Publication History

Article published online:
10 September 2025