A method for detection of atrial fibrillation using RR intervals
Bibliographic record
Abstract
This work describes a method for automatic detection of atrial fibrillation (AF) based on RR intervals. We define /spl Delta/RR to be the difference between successive RR intervals. The standard density histograms of RR and /spl Delta/RR intervals are determined from data in the MIT-BIH atrial fibrillation/flutter database. The present method estimates the similarity between the standard density histograms and a best density histogram by the Kolmogorov-Smirnov (KS) test. The algorithm returns significance (p) of difference between given histograms. If the p value is smaller than a value (P/sub c/), the test density histogram is significantly different from the standard density histogram. If the test density histogram is not significantly different from the standard density histogram, we say the data is AF: Using the standard density histogram of /spl Delta/RR with P/sub c/=0.01, the average sensitivity is 93.2% and the average specificity is 96.7%.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".