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

Front Neurol. 2025 Mar 27:16:1509443. doi: 10.3389/fneur.2025.1509443. eCollection 2025.

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 (n = 4) from MeVO/LVO-negative cases (n = 23). EEG data was acquired using both dry and wet electrode systems, with wet electrodes yielding lower attrition rates arising from superior signal quality.

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.

Keywords: EEG; emergency care; large vessel occlusion; prehospital / EMS; stroke.

Grants and funding

The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the funding provided from the National Science Foundation (NSF) SBIR Phase 1 Award #2213156.