Automated Diagnosis of Neuromuscular Disorders using EMG Signals
Bibliographic record
Abstract
Electromyography (EMG) signals are anatomical and physiological properties representation of muscles; their analysis is crucial for diagnosing neuromuscular diseases, and it typically involves the manual inspection of the signals. However, in recent years, experts have developed automatic classification systems to support practitioners and enhance the diagnostic process. In this study, we have used diverse methodologies to extract features from the raw EMG signal in the time and time-frequency domain, using DWT, WPD and a combination of both, with and without preprocessing and for varying levels of decomposition from level 3 to 10, with the support of higher order stats. We have used optimized ensemble tree and optimized SVM classifiers with Bayesian optimization of hyper parameters. The experiments were conducted using the publicly available dataset EMG lab. We got good training results reaching a 100% accuracy from many combinations and testing accuracy of 78.35% when using DWT without preprocessing at level 9 with the linear SVM classifier. The obtained outcomes demonstrate the effectiveness of the suggested method in accurately categorizing the EMG signals. We have also highlighted that the classifier ensemble trees discriminate better between ALS and healthy and doesn’t recognize well Myopathy. Furthermore, the suggested framework has the potential to support clinicians in the diagnosis of neuromuscular disorders.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".