Feasibility of Machine Learned Intracardiac Electrograms to Predict Postinfarction Ventricular Scar Topography
Bibliographic record
Abstract
BACKGROUND: Accurate delineation of scar patterns is valuable for guiding catheter ablation of ventricular tachycardia. We hypothesized that scar and its pattern of distribution can be determined from intracardiac electrograms using computational signal processing and that further improvements in classification can be achieved with a convolutional neural network. METHODS: A total of 5 sheep underwent anteroseptal infarction (plus 1 healthy control) with electroanatomic mapping (129±12 days post-infarct). A whole-heart histological model of the postinfarction scar was created and coregistered to ventricular electrograms. Electrograms were matched to scar pattern categories; no scar, at least endocardial scar: at least intramural scar (intramural scar sparing the endocardium), or epicardial-only scar (epicardial scar sparing the endocardium/intramural space). A suite of signal-processing features was extracted from bipolar electrograms. Furthermore, bipolar and unipolar electrograms were used to train a time series convolutional neural network (InceptionTime). RESULTS: A total of 11 551 electrograms were matched to 451 biopsies. Bipolar and unipolar voltage alone were poor classifiers of scar patterns. For each of the scar labels, 20 bipolar electrogram features (predominantly within the frequency domain) yielded an area under the curve of 0.815, 0.810, 0.704, and 0.681 to predict no scar, at least endocardial scar, at least intramural scar, and epicardial-only scar, respectively. Substantial improvement was achieved with a convolutional neural network trained on unipolar electrograms: areas under the curve and accuracy (averaged across wavefronts) were 0.977 and 0.929 for no scar, 0.970 and 0.919 for at least endocardial scar, 0.909 and 0.959 for at least intramural scar and 0.926 and 0.958 for epicardial-only scar. CONCLUSIONS: Convolutional neural network-derived analysis of unipolar electrogram data has excellent predictive value for determination of scar patterns. Computational analyses of electrogram data beyond voltage and other time-domain features are necessary to improve the identification of arrhythmogenic sites in the ventricle.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".