BSeg++: A modified Blind Segmentation Method for Ballistocardiogram Cycle Extraction
Bibliographic record
Abstract
This paper presents a method to extract cardiac cycles and H-I-J components of Ballistocardiogram (BCG). The new improved algorithm BSeg++ permits on the segmentation of BCG signal and extraction of its basic complexes H-I-J without Electrocardiogram (ECG) synchronization. The BSeg++ is based on two previously developed methods described in [1, 2, 3] for extracting BCG cycles without using a reference ECG signal. Those methods suffered from extract redundant BCG cycles because of motion artifacts or BCG fluctuations. In this study, we modified the blind segmentation algorithm and solved its problems. We also added another feature to detect H-I-J complexes of BCG. Also, this new algorithm can be used to extract cardiac cycles and R-S-T components of ECG. The data analysis has been performed on the subjects tested at Simon Fraser University. Initial tests of BCG and ECG from twenty subjects indicate that the method extracted BCG (ECG) cycles and its components with a negligible error in the presence of motion artifacts, BCG fluctuations, latency and non-linear disturbance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.005 | 0.003 |
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".