Differences of electroencephalography wave with eyes-closed between older women with dementia and without dementia
Bibliographic record
Abstract
Electroencephalograph (EEG) is an alternative tool to detect brain abnormalities, but research on dementia patients is still limited. This study aimed to determine the differences in EEG waves with closed eyes between older women with dementia and non-dementia. This research uses a cross-sectional method. Examination of dementia using MMSE (Mini-Mental State Examination) with a cut-off value of 23 and examination of brain waves using InteraXon Muse Headband EEG (InteraXon, Canada) for 10 minutes at rest with eyes closed. The study sample consisted of 27 women with dementia and 27 non-dementia women with a mean age of 74.65 years from nursing homes and public health centers in Bandung, Indonesia. Data analysis used independent sample t-test and Mann-Whitney test. The results showed that there were significant differences in Delta AF7 (p = 0.007), Delta TP9 (p = 0.039), Delta TP10 (p = 0.024), and Theta AF7 (p = 0.017). Older women with dementia have lower slow waves (delta and theta waves) than older women without dementia. In conclusion, older women with dementia had decreased EEG waves, including those in Delta AF7, Delta TP9, Delta TP10, and Theta AF7, compared with older women without dementia. Further research can be done with a larger number of respondents and provide stimulation during the EEG examination.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".