A new feature selection method for classification of EMG signals
Bibliographic record
Abstract
Discrimination of neuromuscular diseases based on electromyogram (EMG) is still a hot topic among the rehabilitation society. Although many attempts have been made to elicit informative features from the discretized EMG signals, traditional visual inspection is still their gold-standard method. Therefore, this paper is aimed at introducing an effective combinational feature to enhance the classification rate among the control group and subjects with neuropathy and myopathy diseases. All EMG signals were artificially simulated, by incorporating statistical and morphological properties of each group into their signal models, in the EMG laboratory of Waterloo University. To classify the subjects by the proposed method, first, EMG signals are decomposed by empirical mode decomposition (EMD) to its natural subspaces, then number of subspaces is aligned through all windowed signals, and Kolmogorov Complexity (KC) and other informative feature are determined to reveal the amount of irregularity within each subspace. Finally, these features are applied to support vector machine (SVM). Experimental results show our method can differentiate these three groups efficiently.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".