Increased beat-to-beat T-wave variability in myocardial infarction patients
Bibliographic record
Abstract
The purpose of this study was to investigate the beat-to-beat variability of T-waves (TWV) and to assess the diagnostic capabilities of T-wave-based features for myocardial infarction (MI). A total of 148 recordings of standard 12-lead electrocardiograms (ECGs) from 79 MI patients (22 females, mean age 63±12 years; 57 males, mean age 57±10 years) and 69 recordings from healthy subjects (HS) (17 females, 42±18 years; 52 males, 40±13 years) were studied. For the quantification of beat-to-beat QT intervals in ECG signal, a template-matching algorithm was applied. To study the T-waves beat-to-beat, we measured the angle between T-wave max and T-wave end with respect to Q-wave (∠α) and T-wave amplitudes. We computed the standard deviation (SD) of beat-to-beat T-wave features and QT intervals as markers of variability in T-waves and QT intervals, respectively, for both patients and HS. Moreover, we investigated the differences in the studied features based on gender and age for both groups. Significantly increased TWV and QT interval variability (QTV) were found in MI patients compared to HS (p<0.05). No significant differences were observed based on gender or age. TWV may have some diagnostic attributes that may facilitate identifying patients with MI. In addition, the proposed beat-to-beat angle variability was found to be independent of heart rate variations. Moreover, the proposed feature seems to have higher sensitivity than previously reported feature (QT interval and T-wave amplitude) variability for identifying patients with MI.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".