ConformalShift: Targeted Event Reordering Against Adaptive ECG Monitoring
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Computer Science > Machine Learning
Title:ConformalShift: Targeted Event Reordering Against Adaptive ECG Monitoring
Abstract:Adaptive conformal prediction can recover clinically important heartbeat classes missed by a point classifier, but delayed feedback makes its decisions sensitive to event order. We introduce ConformalShift, a bounded event-reordering attack that suppresses the ventricular class for rescued events without modifying ECG waveforms, labels, classifier scores, or the event multiset. ConformalShift searches for feasible permutations of authentic preceding events that lower the ventricular threshold before a selected target is evaluated. On disjoint MIT--BIH confirmation records, the attack suppressed 66.7% of eligible targets for Extra Trees and 60.0% for HistGradientBoosting, compared with random-schedule rates of 4.4% and 12.0%, respectively. Transferred configurations also outperformed random scheduling on INCART, while reducing the displacement budget weakened the attack on both datasets. These results show that adaptive monitors in healthcare can be compromised through the timing of authentic information, even when waveforms, labels, classifier outputs, and event contents remain unchanged.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.03628 [cs.LG] |
| (or arXiv:2608.03628v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.03628
arXiv-issued DOI via DataCite (pending registration)
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