Evidence map›Paper›PMID 40758428›Full record

ArticleEpilepsia open2025

Frontiers in EEG as a tool for the management of pediatric epilepsy: Past, present, and future.

Hiroki Nariai

Abstract read
In one paragraph

Article in Epilepsia open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Clinical Advances in Child Neurology.Journal of clinical medicine · 2026
    Article
  3. Article
  4. Infantile epileptic spasms syndrome: Mechanisms and therapeutic approaches.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Hiroki NariaiDivision of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-8318-2924

Funding

Refining neurophysiological biomarkers of epilepsy using deep learning to guide pediatric epilepsy surgeryK23NS128318 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Hiroki Nariai · 2023 to 2026
$968k
Children's Discovery and Innovation Institute, University of California, Los Angeles CDI-SEED-010124Elsie and Isaac Fogelman EndowmentNINDS NIH HHS K23 NS128318NINDS NIH HHS K23NS128318Sudha Neelakantan & Venky Harinarayan Charitable Fund
6 · The paper itself

Abstract

Electroencephalography (EEG) has evolved into an indispensable tool in pediatric epilepsy, fundamentally transforming the diagnosis, classification, and management of this condition. This review chronicles the historical journey of EEG from its groundbreaking inception to its current pivotal role in delineating distinct pediatric epilepsy syndromes. Early observations of epileptiform patterns paved the way for the classification of pediatric epilepsy syndromes, such as Childhood Absence Epilepsy, West syndrome, and Lennox-Gastaut syndrome, marking a significant leap in understanding pediatric epilepsy. Over the decades, EEG's application has advanced from mere observation of brain activity to a systematic analysis that not only identifies abnormal patterns but also guides targeted treatment decisions. Currently, EEG assists clinicians in differentiating epileptic events from nonepileptic phenomena, which is crucial for accurate diagnosis and appropriate intervention. By localizing epileptogenic zones, EEG informs surgical planning in drug-resistant epilepsy. Continuous EEG monitoring in pediatric critical care settings enables real-time seizure detection, which can significantly reduce morbidity. Furthermore, EEG plays a critical role in evaluating the efficacy of pharmacological therapies, ensuring that treatments are both timely and tailored to individual patient needs. Emerging conceptual and technological advancements-including quantitative biomarkers for genetic epilepsies, high-frequency oscillations from scalp EEG, network-based analyses, and real-time seizure detection combined with closed-loop stimulation-hold tremendous potential to transform the diagnosis and treatment of pediatric epilepsy. Concurrently, the rapid integration of artificial intelligence is pushing the boundaries of EEG analysis in ways previously unimaginable. These innovations allow for earlier detection of subtle or subclinical pathological EEG signatures and provide prognostic insights that can inform long-term management strategies. As these techniques mature, they promise not only to refine current clinical approaches but also to foster personalized interventions that optimize outcomes for children with epilepsy. PLAIN LANGUAGE SUMMARY: EEG has evolved from Hans Berger's first recordings in 1924 to a critical tool for diagnosing and classifying childhood epilepsy. Noninvasive scalp and video-EEG now distinguish true seizures from other events, monitor treatment response, forecast risk, and guide ICU care. When needed, invasive EEG pinpoints seizure sources for surgery. Cutting-edge methods-like detecting high-frequency oscillations, network analysis, AI-driven interpretation, and closed-loop stimulation-are creating personalized therapies and improving outcomes for children with epilepsy.

Indexed as

AIEpilepsy syndromeHFOILAE

Identifiers

PMID40758428
PMCPMC13394139

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.