Key result
Calibration-aware ECG adaptation requires 56 clusters to detect a ~5 percentage point triage accuracy gain.
Why the study?
Paper ECG recordings remain the dominant medium for cardiac triage in many African healthcare settings, yet chart recorder speed calibration varies across devices and over time, systematically distorting the time axis on which automated interpretation depends.
This study formalizes ECG paper speed calibration variance as a domain adaptation problem and establishes via power analysis that 56 clusters are needed to detect a 5 percentage point improvement in cardiac triage accuracy prior to clinical deployment.
May mitigate ECG misinterpretation from speed drift in low-resource settings; leaves open prospective validation before clinical adoption.
Paper electrocardiogram (ECG) recordings remain the dominant medium for cardiac triage in many African healthcare settings, yet chart recorder speed calibration varies across devices and over time, systematically distorting the time axis on which automated interpretation depends. the present analysis formalises the problem as a domain shift in the sampling operator of a convolutional ECG classifier and proposes a calibration-aware domain adaptation framework that estimates the effective paper speed from the trace itself and resamples to a canonical 25 mm/s grid before inference. Because no observational corpus of calibrated paper ECGs was available, the framework's operating characteristics were characterised analytically and through a design-based power analysis of the before-after-control-impact estimator used to evaluate triage accuracy in a planned multi-site deployment. Under a declared variance structure with between-cluster standard deviation of 6.0 percentage points, residual standard deviation of 4.5 percentage points and intraclass correlation of 0.64, the analysis indicates that 56 clusters are required to detect a 5 percentage point improvement in triage accuracy at 80% power, and that the minimum detectable effect falls steeply as cluster count rises from 8 to 32. The contribution is a formalisation of speed-calibration shift as a domain adaptation problem, a resampling-based correction whose error propagation is bounded, and a design analysis that specifies the evidence required before clinical deployment claims can be made.
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Lutchmun et al. (2026) studied Cardiac triage via paper electrocardiogram. Calibration-aware domain adaptation framework for AI ECG interpretation vs. Standard uncalibrated AI ECG interpretation was evaluated on Healthcare triage accuracy. A calibration-aware ECG domain adaptation framework requires 56 clusters to detect a 5 percentage point improvement in triage accuracy at 80% power (ICC 0.64).
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