Improvement of ballistocardiogram processing by inclusion of respiration information
Bibliographic record
Abstract
In this paper a novel methodology for processing of a ballistocardiogram (BCG) is proposed in which the respiration signal is utilized to improve the averaging of the BCG signal and ultimately the annotation and interpretation of the signal. Previous research works filtered out the respiration signal while the novelty of the current research is that, rather than removing the respiration effect from the signal, we utilize the respiration information to improve the averaging and thus analysis and interpretation of the BCG signal in diagnosis of cardiac malfunctions. This methodology is based on our investigation that BCG cycles corresponding to the inspiration and expiration phases of the respiration cycle are different in morphology. BCG cycles corresponding to the expiration phase of respiration have been proved to be more closely related to each other when compared to cycles corresponding to inspiration, and therefore expiration cycles are better candidates to be selected for the calculation of the averaged BCG signal. The new BCG average calculated based on this methodology is then considered as the representative and a template of the BCG signal for further processing. This template can be considered as the output of a clinical BCG instrument with higher reliability and accuracy compared to the previous processing methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".