Relationship Between EEG Beta Power Abnormality and Early Diagnosis of Cognitive Impairment Post Cerebral Hemorrhage
Bibliographic record
Abstract
Cerebral hemorrhage is a common disease of older adults, which could increase the risk of cognitive impairment. Electroencephalogram (EEG) characteristics can be analyzed to investigate the applied value in the assessment of cognitive impairment of the patients with cerebral hemorrhage. One hundred eighty-two patients (including patients with cognitive impairment [CHCI] and patients with cognitive normality [CHNC] with cerebral hemorrhage, and 120 normal healthy persons [control; CN]) were recruited between July 2008 to March 2012 at the department of neurology. All patients were analyzed by EEG, and analysis results were compared to the Montreal Cognitive Assessment (MoCA) scale, using the methods of correlation analysis, clustering analysis, and concordance analysis. The results indicated that patients with CHCI had significantly lower EEG beta power (0.814 ± 0.113 mcV(2)) relative to CHNC (1.601 ± 0.186 mcV(2), P < .01) or CN group (1.713 ± 0.201 mcV(2), P < .01). Significant negative correlation was found between the beta power and hemorrhage region, age, hemorrhage size, hemorrhage amount (r 1 = -.92223, r 2 = -.81084, r 3 = -.79258, r 4 = -.84961, respectively, all P < .001). There was good concordance between K-means clustering algorithm calculating the beta power and MoCA scoring (Kappa = 0.899, P < .001). In conclusion, the preliminary findings suggest that the recognition techniques of EEG hold considerable promise for the assessment of cognitive impairment post cerebral hemorrhage, which negatively related to the hemorrhage region, hemorrhage size, hemorrhage amount, and age.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".