Bibliographic record
Abstract
Electromyogram (EMG) signals can provide valuable insights into muscle activation and health; however, achieving high-quality signals is challenging.EMG signals are susceptible to contamination, and the acquisition of high-quality EMG signals often requires lengthy setup times and experienced technicians.EMG signal quality analysis is an important domain of research to increase EMG integration into clinical practice, wearable devices, and long-term monitoring.This thesis develops techniques to assess EMG signal quality to improve the ease of EMG acquisition.A literature review of methods for EMG signal quality analysis was conducted.Key areas for further research were identified, including improved selection of high-quality channels within EMG arrays.A process for establishing a ground truth of EMG signal quality based on expert human ratings was developed and used to label an EMG dataset.The intra-rater reliability ranged from 0.76-0.90, and the inter-rater agreement was 0.83, indicating good reliability and that the human rated datasets could be used for future assessments.An automated method of quantifying EMG signal quality was developed in two stages.In Stage I, poor-quality EMG channels were detected and reconstructed.An interpolation-based method of detecting and reconstructing poor-quality channels in high-density EMG (HD-EMG) was developed.Compared with two other detection methods (root mean square and normalized mutual information), the interpolation-based method was most effective for identifying poor iii channels in HD-EMG (precision 95.0%, recall 100%).Reconstruction of poor-quality channels was best performed with a 2-dimensional spline interpolation (correlation = 0.99).In Stage II, machine learning was explored to distinguish between adequate, good, and excellent channels.A random forest regression model provided the best overall result with Spearman correlations of 0.90, 0.91, 0.74, and 0.73 for inter-electrode distances of 10, 20, 30, and 40 mm, respectively.The recommended EMG feature set was an amplitude-based feature (e.g., root mean square) combined with frequency-based feature(s) (e.g., mean frequency).In single channel EMG, the Spearman correlation dropped to 0.55; however, there was potential to distinguish between adequate and excellent quality channels.The automated methods for quantifying EMG signal quality developed in this thesis will lead to improved efficiencies and lower costs for EMG acquisition.Ph.D. journey.I truly could not have asked for a more knowledgeable, dedicated, and supportive supervisor.I would like to extend my sincere thanks to Dr. Dawn MacIsaac for lending her immense expertise and insight to
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".