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Updated: May 11, 2026
Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
Published on: July 14, 2023
Early prediction of poor outcome in severe hemispheric stroke by EEG patterns and gradings
Ying Ying Su1, Miao Wang, Wei Bi Chen
1Neurological Intensive Care Unit, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China. suyingying@xwh.ccmu.edu.cn
Objectives:
To find out the EEG abnormal patterns in massive cerebral hemispheric infarction (MCHI) and their correlation with poor outcome, and to construct an EEG grading for predicting the outcome of MCHI patients.
Methods:
Between 2000 and 2010, 162 patients with MCHI who met the selection criterions were selected for this study. All the patients underwent EEG examinations within 3 days after stroke onset and repeated on day 2 and 3. We classified the EEG recordings into 9 patterns and anglicized the correlation between EEG patterns and outcome. Then according to the results of the correlation between EEG patterns and outcome we constructed an EEG grading for predicting the outcome of MCHI patients.
Results:
We revealed that patterns of dominant alpha without reactivity, RAWOD, burst-suppression, α/θ-coma, epileptiform activity (without burst-suppression), and generalized suppression were correlated to poor outcome. We further modified the Young grading according to the correlation between EEG patterns and outcome. We found that the modified grading was superior to existing EEG gradings in predicting the outcome of MCHI patients, and it could predict the outcome of MCHI more accurately.
Conclusions:
MCHI is common in N-ICU (Neurology Intensive Care Unit). The EEG analysis would detect the degree of brain lesion during the ischemia within the acute stage after stroke onset. The EEG evaluation might assist the neurophysicians to predict outcome of patients and make decisions on the treatments.
Insights
Electroencephalogram (EEG) patterns in massive cerebral hemispheric infarction (MCHI) correlate with patient outcomes. A new EEG grading system accurately predicts MCHI prognosis, aiding clinical decision-making.
Area of Science:
- Neurology
- Neurophysiology
- Clinical Neuroscience
Background:
- Massive cerebral hemispheric infarction (MCHI) is a severe neurological condition.
- Predicting outcomes in MCHI patients is crucial for treatment planning.
- Existing electroencephalogram (EEG) grading systems may not fully capture MCHI prognosis.
Purpose of the Study:
- To identify abnormal EEG patterns associated with poor outcomes in MCHI patients.
- To develop and validate a novel EEG grading system for predicting MCHI prognosis.
- To compare the predictive accuracy of the new grading system with existing methods.
Main Methods:
- A cohort of 162 MCHI patients was studied between 2000 and 2010.
- EEG examinations were performed within 3 days of stroke onset and repeated.
- EEG recordings were classified into 9 patterns, and correlations with outcomes were analyzed.
Main Results:
- Specific EEG patterns, including burst-suppression and α/θ-coma, were linked to poor outcomes.
- A modified Young grading system, based on EEG pattern correlations, was developed.
- The modified EEG grading demonstrated superior accuracy in predicting MCHI patient outcomes compared to existing systems.
Conclusions:
- EEG analysis in the acute stage of MCHI can reveal the extent of brain lesions.
- Abnormal EEG patterns are significant predictors of poor prognosis in MCHI.
- The developed EEG grading system offers a valuable tool for neurophysicians to predict MCHI outcomes and guide treatment decisions.
