Novel Method for Synchronization of Multiple Biosensors
Bibliographic record
Abstract
Synchronization of signals in the post-trial analysis is a laborious process that is often a bottleneck during the signal analysis. As the concept of the Internet-of-Things (IoT) emerges and more sensors are implemented in a research trial, reliable synchronization schemes are becoming increasingly important. This article presents a synchronization algorithm that could align signals recorded by different platforms with different sampling frequencies to millisecond-level precision. The algorithm could also realign a recording that has been restarted after a device failure with the same alignment precision as the rest of the signals. The algorithm generates a secondary signal by permutation of six different step voltages in each cycle to produce a unique pattern before returning to a baseline. The algorithm has been deployed in an actual clinical trial involving 26 heart failure patients and five different bio-signal modalities. It has successfully aligned all trials, including one trial that had a device failure during the recording. Two aligned heart signals had an average beat-to-beat interval difference of 0.81 ± 0.79 ms or 0.90 ± 0.87% with no sign of a negative effect of the synchronization algorithm.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".