Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus
Bibliographic record
Abstract
EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment (MCI) in T2DM. To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM, six entropies combining empirical mode decomposition (EMD), including Approximate entropy (ApEn), Sample entropy (SaEn), Fuzzy entropy (FEn), Permutation entropy (PEn), Power spectrum entropy (PsEn) and Wavelet entropy (WEn) were used in the study. A feature extraction technique based on maximization of the area under the curve (AUC) and a support vector machine (SVM) were subsequently used to for features selection and classification. Finally, Pearson's linear correlation was employed to study associations between these entropies and cognitive functions. Compared to other entropies, FEn had a higher classification accuracy, sensitivity and specificity of 68%, 67.1% and 71.9%, respectively. Top 43 salient features achieved classification accuracy, sensitivity and specificity of 73.8%, 72.3% and 77.9%, respectively. P4, T4 and C4 were the highest ranking salient electrodes. Correlation analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7, F8 and P4, and PsEn based on EMD was positively correlated to Montreal cognitive assessment (MoCA) and memory at electrode T4. In sum, FEn based on EMD in right-temporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".