Key result
Benchmarking wearable arrhythmia detectors requires 54 clusters to detect a ~5-point change in false alarms.
Why the study?
Wearable-triggered arrhythmia detectors are benchmarked against reference annotations with uncertain atrial fibrillation episode boundaries, and the effect of this boundary ambiguity on reported false-alarm performance has not been characterised.
Provides a boundary-aware benchmarking protocol and statistical design parameters to separate wearable arrhythmia detector error from reference annotation error when evaluating false-alarm performance.
Boundary ambiguity may distort reported false-alarm rates in wearable AF detectors; simulation framework leaves open the magnitude of bias in practice.
Wearable-triggered arrhythmia detectors are benchmarked against reference annotations whose atrial fibrillation episode boundaries are themselves uncertain, yet the effect of that boundary ambiguity on reported false-alarm performance has not been characterised. The framework is then embedded in a before-after-control-impact design with a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64) and evaluated across 2000 replications per design cell for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points.54 clusters are required to detect a 5 percentage point change at 80% power, and that the minimum detectable effect at 80% power falls steeply as clusters increase from 8 to 32. The contribution is a boundary-aware benchmarking protocol that separates detector error from annotation error, together with the design parameters needed to evaluate it credibly.
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Hoareau et al. (2026) studied Atrial fibrillation. Boundary-aware benchmarking protocol for wearable-triggered arrhythmia detectors vs. Standard reference annotations with episode boundary ambiguity was evaluated on False-alarm performance under episode boundary ambiguity (minimum detectable effect at 80% power). Boundary-aware benchmarking of wearable arrhythmia detectors requires 54 clusters to detect a 5 percentage point false-alarm change at 80% power (ICC=0.64).
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