Quantitative EEG analysis related to coma and prognosis in acute severe cerebral infarction

This study delineates distinctive EEG signatures characterizing severe cerebral infarction, establishing non-ipsilesional occipital qEEG parameters as pivotal biomarkers for both consciousness evaluation and outcome prediction. These objective electrophysiological markers not only differentiate LHI from BSI through network-level neural reorganization patterns, but also demonstrate clinical applicability. The findings provide empirical support for qEEG integration into neurocritical care for acute stroke with impaired consciousness.

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Publication Details

Journal
European journal of medical research
Published
2025-09-29
DOI
https://doi.org/10.1186/s40001-025-03191-1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00

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article

Quantitative EEG analysis related to coma and prognosis in acute severe cerebral infarction

Shirui Wen, Xiaobo Zhang, Sha Huang, Li Feng et al.
European journal of medical research
EEG and Brain-Computer Interfaces
article

Quantitative EEG analysis related to coma and prognosis in acute severe cerebral infarction

Shirui Wen, Xiaobo Zhang, Sha Huang, Li Feng, Pengyu Li
article en

Abstract

This study delineates distinctive EEG signatures characterizing severe cerebral infarction, establishing non-ipsilesional occipital qEEG parameters as pivotal biomarkers for both consciousness evaluation and outcome prediction. These objective electrophysiological markers not only differentiate LHI from BSI through network-level neural reorganization patterns, but also demonstrate clinical applicability. The findings provide empirical support for qEEG integration into neurocritical care for acute stroke with impaired consciousness.

European journal of medical researchVol. 30(1)
Central South University (CN), The First People's Hospital of Changde (CN), Xiangya Hospital Central South University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 73%
EEG and Brain-Computer Interfaces
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