Window-level AF detector: feasibility results
A 0.5 MB gradient-boosting classifier on RR-interval and beat-morphology features reaches 98.7 % sensitivity and 98.6 % specificity per 30-second sensor strip on patients never seen in training.
Feasibility study for an atrial-fibrillation (AF) decision stage built on top of the existing beat detector. The beat detector and the per-beat measurements are unchanged; the AF decision is a new window-level classifier. All results below are on data the model never trained on; operating points were chosen on separate tuning records, never on a test set. Status: feasibility, not validation. Last update: 14 September 2026, 23:00.
Headline results
Sensor strips (30 s), 47 held-out patients, 755 strips, no expert artefact flags used. Product rule: 16-beat windows; AF if the highest window probability is at least 0.8, not AF if at most 0.5, otherwise inconclusive; strips without plausible beats are inconclusive.
| Metric | Value |
|---|---|
| Inconclusive | 31.8 % (28 % of this set is artefact by the expert’s flags) |
| Classified strips | 515 |
| Sensitivity | 98.7 % |
| Specificity | 98.6 % |
| PPV / NPV | 96.9 % / 99.4 % |
| Accuracy | 98.6 % |
| Errors | 5 false positives, 2 misses |
Public benchmark, MIT-BIH Atrial Fibrillation Database (23 records, 10 h each), never used in training, RR features only.
| Metric | Value |
|---|---|
| Window sensitivity / specificity | 95.6 % / 96.7 % |
| EC57 duration sensitivity / positive predictivity | 96.5 % / 92.0 % (94.0 % with the two-scale cascade) |
| Episodes of 30 s or longer detected | 100 % |
| Episodes of 10-30 s detected | 89-100 % depending on window scale |
| EC57 episode positive predictivity, all lengths | 47-56 %: short false episodes in ectopy-rich stretches, see results |
Beat detector (unchanged), MIT-BIH Arrhythmia Database, bxb rule: sensitivity 98.1 %, positive predictivity 98.7 %.
What this shows
- Per-strip performance on unseen sensor patients is at the level reported by cleared consumer ECG apps (about 98 % / 99.6 % with roughly 12 % unclassifiable), on a set with far more artefact.
- The long-recording benchmark confirms the algorithm is sound on cardiologist-annotated public data: duration metrics above 92 % and every AF episode of clinical length detected.
- The remaining weakness, short false episodes in long recordings, does not apply to a 30-second strip claim; the pending morphology test targets it for a future continuous-monitoring claim.
Caveats
- Sensor labels are whole-strip annotations made by the original developer; cardiologist re-annotation is the next step and may move the numbers either way.
- 165 AF-positive strips give a lower 95 % confidence bound near 95 % for sensitivity; more annotated strips are needed for tighter bounds.
- The public benchmark was used during exploration before the protocol was frozen; the validation of record will be a single pre-specified run.
- The EC57 scorer is a Python implementation of the published definitions; cross-check with the WFDB tools is pending.
Pages: the algorithm, article-style, methods, data and audit, detailed results, plan to certification, gap analysis and to-do list. Downloads: feasibility report, draft (PDF), full experiment log (Markdown).
In this section
The algorithm: a window-level AF detector for 30-second single-lead ECG
How the detector works, from the cardiac cycle to the strip decision: the signal, the beat measurements it reuses, the rhythm features it computes, the model, the safeguards, and what the data shows.
Methods
Window features on RR intervals and engine beat measurements, gradient boosting with monotonic constraints, a physiological guard, hysteresis episodes and an inconclusive output.
Data and audit
LTAFDB, MIT-BIH, a clinical AF set and sensor strips for training; AFDB and held-out sensor patients for testing; a label audit found and fixed a loader bug and two classes of bad windows.
Detailed results
End-to-end sensor results, EC57 benchmark tables, sensitivity by episode length, false-episode analysis and the comparison with the previous model.
Plan to certification
Certify the 30-second strip claim first; what is met, what is missing, and the order of work.
Gap analysis and to-do list
FDA 510(k) evidence for the 30-second AF strip classifier: what exists, what is missing, who owns it, and in what order.