Empirical mode decomposition analysis of alcohol withdrawal tremor signals
Bibliographic record
Abstract
In this paper, we have introduced a novel method to extract involuntary tremor movement activity in patients with Alcohol Withdrawal Syndrome (AWS). Using the Empirical Mode Decomposition (EMD), we show that the variations of energy of the tremor and voluntary activity can be distinguished in different Intrinsic Mode Functions (IMF) of the recorded tremor signals. To measure the performance of our method in extracting the tremor activity and eventually developing a shortened, more objective AWS assessment tool, we compared the electronic tremor assessment employing our new technique with the consensus rating from 3 expert physicians on a 7-point scale. Based on a 3-fold cross-validation on 104 recordings from 64 patients with AW, we found that our proposed method achieves an average Root Mean Squared Error (RMSE) of 0.71 with respect to the consensus rating, a significant improvement over prior work.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".