Bibliographic record
Abstract
In this paper, we propose an algorithm to quantify the reliability of heart rates estimated from electrocardiogram (ECG) data. Long-term ECG monitoring is becoming more prevalent with an increasing number of ambulatory monitors, including the Smart Rollator which is a rollator equipped with a series of minimally obtrusive sensors. The ECG reliability index enables the automatic pre-processing of data, to highlight or discard data that are corrupted by noise or other artifacts. Three reliability indices are proposed that are based on the assumption of a short-term invariant PQRST waveform. These indices use distance measures to quantify the reliability of the ECG, which are: 1) percent residual difference, 2) cross-correlation coefficient, and 3) a wavelet distance measure. The reliability indices are evaluated using real ECG data corrupted with three types of noise: 1) baseline wander, 2) electromyogram artifacts, and 3) motion artifact. All three reliability indices demonstrate an ability to track the accuracy of heart rate estimates derived from the ECG. Among the three indices investigated in this work, the wavelet distance measure provides the best performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".