Bibliographic record
Abstract
Advances in wearable sensors have enabled physiological monitoring in scenarios outside of medical clinics. Two main areas, the cardiovascular and respiratory monitoring, generally require separate devices to analyze their performance. Seismocardiography (SCG) has the potential to monitor both aspects as it records the motion of the chest wall. SCG has been well developed for cardiovascular monitoring, however, there is limited knowledge on the effects of respiration. The objective of this thesis is to characterize how SCG is affected by respiration and how this effect can be used to derive respiratory metrics. 50 subjects were recorded at McGill University with an inertial measurement unit on the xiphoid process and a spirometer was used for reference. Three scenarios were examined: the subject at rest, the subject during breath holds when inhaled, and the subject during breath holds when exhaled. Respiration modulation of the signals were explored in three main areas: amplitude modulation, frequency modulation, and baseline wandering. It was found that the SCG amplitude was dependent on respiratory volume in both the rest and apnea states. Baseline modulation and frequency modulation were dependent on inhalation and therefore showed no effects in the apnea state. Respiration Rate was extracted from all methods, with baseline wandering producing a maximum r-squared value of 0.71. The phase of baseline and frequency modulation showed better alignment with inhalation rather than volume. Amplitude modulation had less bias in respiratory phase prediction but higher standard deviation. A high inter-subject variability caused inconsistent results for extracting tidal volume with this method. Hence, no conclusive linear relationship with tidal volume was found. Given some calibration, the respiratory parameters of most subjects could be predicted using SCG. More work should be explored as to why only some subjects display strong correlation while others do not. SCG is a powerful cardiovascular monitoring tool and can potentially be developed into a respiratory monitoring tool to facilitate an integrated, non invasive cardio respiratory monitoring system
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".