Abstract 17684: A Novel System for the Rapid and Automated Detection of Atrial Fibrillation
Bibliographic record
Abstract
Introduction: Home telemetry monitoring with accurate automated rhythm classification can have important clinical benefits in the timely diagnosis and appropriate management of patients with atrial fibrillation (AF). We clinically validated a novel personal e-Health device and algorithm developed to distinguish AF from sinus rhythm (SR). Methods: A handheld electrocardiogram (ECG) recording system (Maestro) and signal processing platform were developed. The Maestro provides an LCD interface that continuously shows an ECG, heart rate, and heart rhythm status. Twenty second ECG signals analogous to Lead I were acquired from 66 patients presenting to the arrhythmia clinic at the University of Michigan Hospital either in SR or AF. Electrograms were segmented into non-overlapping 6-second samples and one random segment per patient was selected for analysis by the Maestro system. Simultaneous 5 or 12 lead ECGs were obtained from these patients and 3 expert physicians blinded to the Maestro analysis identified the rhythm as SR or AF. The Maestro system applied several signal conditioning algorithms to each ECG sample. The dimensionless temporal R-R interval variability (VRR) index and spectral frequency dispersion metric (FDM) were computed. Results: The 2-dimensional scatter-gram of the samples demonstrated 2 distinct clusters of VRR and FDM for patients with SR and AF. The VRR index clusters for SR and AF patients were 0.018 ± 0.013 and 0.187 ± 0.073 (mean±std), respectively (p < 0.001). The FDM clusters for SR and AF patients occurred at 10.5 ± 5.916 and 15.892 ± 3.337, respectively (p < 0.001). We developed a Gaussian Mixed Model (GMM) classifier to distinguish between the AF and SR clusters. Only after the GMM classifier was obtained were the Maestro classifications compared to the physicians’ readings. The algorithm correctly categorized AF (N = 46) and SR (N = 20) for all Maestro segments analyzed with 100% specificity and sensitivity. Conclusion: The Maestro handheld telemetry unit utilizes a novel classification algorithm and was demonstrated to acquire and automatically analyze 6-second electrograms for rapid and accurate classification of patients in SR or AF in this initial clinical validation trial.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".