The role of surface EMG in predicting responsiveness of muscles to FES therapy after cervical SCI
Bibliographic record
Abstract
INTRODUCTION: Cervical spinal cord injury (SCI) can severely impair upper extremity (UE) functions, limiting independence and quality of life. Prior clinical trials showed that functional electrical stimulation (FES) therapy can reduce UE impairment. However, the response to FES therapy is not consistent across all treated myotomes. Our objective was to predict the muscle response to FES therapy using electrophysiological biomarkers from baseline surface electromyography (sEMG) signals, in order to support treatment decisions at the point of care. METHODS: We recruited 17 participants with cervical SCI, who were about to undergo FES therapy. Target UE muscles were identified for each participant by treating therapists. Baseline sEMG signals were recorded from the target muscles during resting and maximal voluntary contractions. Time- and frequency-domain features were extracted. The manual muscle testing (MMT) score was tracked through the therapy cycle, and used to categorize each muscle as a responder or non-responder. We explored classifiers including support vector machines, k-nearest neighbors, random forest, and logistic regression, with leave-one-participant-out cross validation. Models were trained on sEMG features alone, on clinical variables alone, and combinations of both. RESULTS: The final dataset consisted of sEMG recordings of 132 muscles from 17 participants, and 33% of the muscles were considered responders. A Random Forest classifier with a forward-selected feature set yielded the best performance (Matthews correlation coefficient = 0.41, F1 score = 0.68, accuracy = 76%, precision = 0.72, recall = 0.42, and true negative rate = 0.92). With patient stratification based on motor completeness (AIS A-B vs. C-D), the model performance further improved. Included signal features were slope sign changes, mean and median frequency, and second-order spectral moment. CONCLUSIONS: Our results suggest that baseline sEMG signals combined with machine learning models may be used to predict muscle response to FES therapy in individuals with cervical SCI. The models were trained on a small and unbalanced sample and can be optimized with more participants in the future. This work contributes to improving the level of personalization and efficacy of FES therapy, and ultimately improve quality of life after SCI.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".