Evidence map›Paper›PMID 40212611›Full record

ArticleFrontiers in neurology2025

Exploring the feasibility of EEG for pre-hospital detection of medium and large vessel occlusion strokes: a proof-of-concept study.

William Peterson, Nithya Ramakrishnan, David Tinklepaugh, Adrian Hamburger, Arthur Kowell, Krag Browder, Nerses Sanossian, Peggy Nguyen, Ezekiel Fink

Abstract read
In one paragraph

Article in Frontiers in neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

William PetersonAsterion AI, Dallas, TX, United States.
Nithya RamakrishnanAsterion AI, Dallas, TX, United States.
David TinklepaughAsterion AI, Dallas, TX, United States.
Adrian HamburgerAsterion AI, Dallas, TX, United States.
Arthur KowellDepartment of Neurology, UCLA Health, Los Angeles, CA, United States.
Krag BrowderAsterion AI, Dallas, TX, United States.
Nerses SanossianRoxanna Todd Hodges Stroke Program, University of Southern California, Los Angeles, CA, United States.
Peggy NguyenDepartment of Neurology, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States.
Ezekiel FinkAsterion AI, Dallas, TX, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Early and accurate identification of stroke subtypes, particularly medium (MeVO) and large vessel occlusions (LVO), is critical for timely intervention and improving patient outcomes. Current pre-hospital diagnostic methods are limited in sensitivity, delaying treatment for ischemic stroke candidates eligible for endovascular thrombectomy (EVT). Methods: This proof-of-concept study explores the feasibility of using electroencephalography (EEG) as a diagnostic tool for pre-hospital detection of MeVO and LVO strokes. Conducted in the emergency department setting, this study assessed the efficacy of quantitative EEG biomarkers in differentiating MeVO/LVO-positive cases ( Results: Findings from MeVO- and LVO-positive subjects revealed hemispheric asymmetry in delta and alpha frequency bands, particularly in frontal and temporal regions, as well as a global attenuation of power irrespective of the region of stroke. Discussion: This study supports the potential of EEG for real-time, non-invasive stroke detection in pre-hospital and clinical environments, demonstrating the need for wet EEG systems for reliable signal acquisition. Future work aims to validate the use of EEG in the pre-hospital setting in an effort to facilitate rapid triage and reduce time to treatment for stroke patients.

Indexed as

EEGemergency carelarge vessel occlusionprehospital / EMSstroke

Identifiers

PMID40212611
PMCPMC11983880

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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.