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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".