Automated detection of atrial fibrillation episode using novel heart rate variability features
Why this work is in the frame
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Bibliographic record
Abstract
Atrial fibrillation (AF) is one of the most common life-threatening arrhythmia affecting around six million adults in the US. Typical detection of AF requires tedious and manual analysis of ECG which can often delay medical intervention. With the advent of wearable devices that can accurately record the time interval between two heartbeats (RR interval), automated and timely detection of AF is now possible. In this paper, we engineer novel heart rate variability features based on linear and non-linear dynamics of RR intervals. Unlike complex features extracted from ECG signals, these features can be easily obtained using wearable sensors. We propose automated classifiers to detect AF episodes and also compare the performance of different classifiers. Our proposed classifier has a very high sensitivity (98%) and specificity (95%) and outperforms prior published works.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it