2013 39th Annual Northeast Bioengineering Conference Cross-Comparison Between Two Multi-Channel EMG Decomposition Algorithms Assessed with Experimental and Simulated data
Bibliographic record
Abstract
Abstract—The reliability of automated electromyogram (EMG) decomposition algorithms is important in clinical and scientific studies. In this paper, we analyzed the performance of two multi-channel decomposition algorithms—Montreal [1] and Fuzzy Expert [2] using both experimental and simulated data. Comparison data consisted of quadrifiler needle EMG from the tibialis anterior muscle of 12 subjects (young and elderly) at three contraction levels (10, 20 and 50 % MVC), and matched simulation data. Performance was assessed via agreement between the two algorithms for experimental data and accuracy with respect to the known decomposition for simulated data. For the experimental data, median agreement between the Montreal and Fuzzy Expert algorithms at 10, 20 and 50 % MVC was 95.7, 86.4 and 64.8%, respectively. For the simulation data, median accuracy was 99.8%, 100 % and
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".