Novel frequency analysis of signal‐averaged electrocardiograms is predictive of adverse outcomes in implantable cardioverter defibrillator patients
Bibliographic record
Abstract
BACKGROUND: Current noninvasive risk stratification methods offer limited prediction of arrhythmic events when selecting patients for ICD implantation. Our laboratory has recently developed a signal processing metric called Layered Symbolic Decomposition frequency (LSDf) that quantifies the percentage of hidden QRS wave frequency components in signal-averaged ECG (SAECG) recordings. The purpose of this pilot study was to determine whether LSDf can be predictive of ventricular arrhythmia or death in an ICD patient cohort. METHODS AND RESULTS: Fifty-two ICD patients were recruited from 2008 to 2009. These were followed for a mean of 8.5 ± 0.4 years for the primary outcome of first appropriately treated ventricular arrhythmia (VT/VF) or death. Thirty-four subjects met the primary outcome. LSDf was significantly lower, and 12-lead QRS duration was significantly greater in patients meeting the primary outcome (12.14 ± 3.97% vs. 16.45 ± 3.73%; p = 0.001) and (111.59 ± 14.96 ms vs. 97.69 ± 13.51 ms; p = 0.012) respectively. A 13.25% LSDf threshold (0.74 sensitivity and 0.85 specificity) was selected based on an ROC curve. Kaplan-Meier survival analysis was conducted; patients above the 13.25% threshold demonstrated significantly better survival outcomes (log-rank p < 0.001). In Cox multivariate regression analysis, the LSDf threshold (13.25%) was compared to LVEF (28.5%), 12-lead QRSd (100 ms), age, % male sex, NYHA classification, and antiarrhythmic usage. LSDf was a predictor of the primary outcome (p = 0.005) and an independent predictor for solely ventricular arrhythmia (p = 0.002). CONCLUSION: Layered Symbolic Decomposition frequency analysis in SAECG recordings may be a viable predictor of negative ICD survival outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".