Electroencephalogram beta power assay: A promising diagnosis tool of cognitive impairment in early time after cerebral hemorrhage
Bibliographic record
Abstract
BACKGROUND: Cerebral hemorrhage (CH) could affect the cerebral function on specific cognitive abilities and lead to the cognitive decline or cognitive dysfunction. Electroencephalogram (EEG) is a relatively cheap and easy usable tool, which could reflect the cerebral function of the patients. MATERIALS AND METHODS: A total of 170 patients (patients with and without cognitive impairment) with CH and 120 normal healthy controls were recruited from September 2008 to June 2012 at the Department of Neurology. EEG studies were carried out to analyze the cerebral function in all the subjects. Correlation, clustering and concordance analysis were performed to analyze the relationship between EEG power and Montreal cognitive assessment (MoCA) scores. The effects of EEG analysis were assessed to diagnosis the cognitive impairment. RESULTS: The results were showed that patients with cognitive impairment had a significantly decreased EEG beta power (0.771 ± 0.149 μV 2 ) compared with the normal cognitive function (1.654 ± 0.186 μV 2 , P < 0.01) or normal healthy controls (1.703 ± 0.216 μV 2 , P < 0.01). Significantly positive correlation (r = 0.90174, P < 0.001) was discovered between relative beta power and hemorrhage type, while significantly negative correlations between the relative beta power and hemorrhage size and amount were also observed (r =-0.81235 and r =-0.90136, respectively, all P < 0.001). There was a better concordance between K-means clustering algorithm calculating of the relative beta power and MoCA scores (κ =0.913, P < 0.001). CONCLUSION: The cognitive impairment post hemorrhage was positively correlated to hemorrhage type and negatively correlated with hemorrhage size and amount. The analysis method of EEG beta power abnormality holds a promise to assess the cognitive impairment post CH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".