Evaluation of a subject-specific transfer-function-based nonlinear QT interval rate-correction method
Bibliographic record
Abstract
The QT interval in the electrocardiogram (ECG) is a measure of total duration of depolarization and repolarization. Correction for heart rate is necessary to provide a single intrinsic physiological value that can be compared between subjects and within the same subject under different conditions. Standard formulas for the corrected QT (QTc) do not fully reproduce the complexity of the dependence in the preceding interbeat intervals (RR) and inter-subject variability. In this paper, a subject-specific, nonlinear, transfer function-based correction method is formulated to compute the QTc from Holter ECG recordings. The model includes five parameters: three describing the static QT-RR relationship and two representing memory/hysteresis effects that intervene in the calculation of effective RR values. The parameter identification procedure is designed to minimize QTc fluctuations and enforce zero correlation between QTc and effective RR. Weighted regression is used to better handle unbalanced or skewed RR distributions. The proposed optimization approach provides a general mathematical framework for further extensions of the model. Validation, robustness evaluation and comparison with existing QT correction formulas is performed on ECG signals recorded during sinus rhythm, atrial pacing, tilt-table tests, stress tests and atrial flutter (29 subjects in total). The resulting average modeling error on the QTc is 4.9 ± 1.1 ms with a sampling interval of 2 ms, which outperforms correction formulas currently used. The results demonstrate the benefits of subject-specific rate correction and hysteresis reduction.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".