Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
Bibliographic record
Abstract
Evaluating respiratory drive presents challenges due to the obtrusiveness and impracticality of current methods like functional magnetic resonance imaging (fMRI). Electromyography (EMG) offers a surrogate measure of respiratory drive to the muscles, allowing the determination of both the magnitude and timing of muscle activation. The magnitude reflects the level of muscle activation, while the timing indicates the onset and offset of muscle activity relative to specific events, such as inspiratory flow and activation of other muscles. These metrics are critical for understanding respiratory coordination and control, especially under varying loads or in the presence of respiratory pathophysiology. This study outlines a protocol for acquiring and analyzing respiratory muscle EMG signals in healthy adults and patients with respiratory health conditions. Ethical approval was obtained for the studies, which included participant preparation, electrode placement, signal acquisition, preprocessing, and postprocessing. Key steps involve cleaning the skin, locating muscles via palpation and ultrasound, and applying electrodes to minimize electrocardiography (ECG) contamination. Data is acquired at a high sampling rate and gain, with synchronized ECG and respiratory flow recordings. Preprocessing includes filtering and transforming the EMG signal, while postprocessing involves calculating onset and offset differences relative to the inspiratory flow. Representative data from a healthy male participant performing incremental inspiratory threshold loading (ITL) illustrate the protocol's application. Results showed earlier activation and prolonged duration of extradiaphragmatic muscles under higher loads, correlating with increased EMG magnitude. This protocol facilitates a detailed assessment of respiratory muscle activation, providing insights into both normal and pathophysiologic motor control strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".