Towards increased usability of noisy ECG signals in HRV-based classifiers
Bibliographic record
Abstract
Proliferation of wearable devices and the “quantified self” behavior creates massive amounts of data acquired from physiological sensors. Unfortunately this vast amount of data is to date largely untapped for clinical purposes because of low data reliability caused by issues such as contact noise and motion artifacts. One important physiological signal is ECG and one of its important features is the heart rate, obtained from the QRS complex of the ECG signal or beat detection algorithms. This feature is important in characterizing Heart Rate Variability (HRV) and detecting various pathologies such as arrhythmia, chronic heart failure, or sleep apnea. Many such algorithms simply discard data segments which are deemed “unreliable” due to errors in QRS detection. This paper analyzes the impact of changing the noise level and noise duration on the percentage of data segments that are discarded. The paper proposes an approach to improve the usability of ECG data corrupted by noise by analyzing the impact of noise on features of interest and adapting relevant system parameters accordingly. The proposed approach can be used with any classifier operating on short-term HRV features.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".