Effect of chest morphology on vibrational cardiography waveforms using multi-sensor analysis
Bibliographic record
Abstract
Vibrational waves generated by cardiac events can be detected by an accelerometer or a gyroscope placed on the surface of the chest. These waves are generally in the infrasonic range and contain information about cardiac mechanics. Recent advances in sensor technology have paved the way to portable, and non-invasive sensors. Wearable devices developed with these sensors can be used to monitor cardiac vibrations continuously, providing early detection of cardiovascular diseases. These are especially important in rural areas or even for astronauts in the space, that is, in places where proper clinical support is often unavailable. However, these vibrations are extremely sensitive to sensor placement. Hence a better understanding of the waves in connection the human body is necessary to take this system from the lab to a hospital setting, and our households. This thesis studies cardiac vibrations through acceleration recorded on seven locations on the chest. Previous studies have attempted to investigate chest vibrations through physical testing, analytical solution, and simulation. However, any connection between morphological changes in the chest and vibrational cardiography (VCG) signal quality is yet to be established. In this study, we propose a novel method to connect the vibrations to the changes in body composition level by testing in the lab, and by building simple numerical models of the human chest containing individual material properties for the organs within. A multi-sensor based simultaneous VCG recording system was developed in the lab and detailed in this thesis. A pilot test was performed on three subjects at McGill University, during the restrictions placed by the Covid-19 pandemic. One participant underwent a weight loss and strength training program and testing revealed that the signal-to-noise ratio of cardiac vibrations improved during that period. Amplitude modulation due to different sensor positioning was observed and it helped to identify the best locations on the chest for VCG recording. All sensor positions were able to pick up cardiac valvular activity. The systolic and the diastolic peak of the vibrational waves were studied simultaneously in time and frequency domain and revealed similar frequency contents in the signal during both events. A detailed study on a larger population must be completed to validate the findings
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".