Prefix-Adaptive and Utility-Aware Stopping for EEG Closed-Loop Resource-Efficient Sleep Staging

Continuous EEG acquisition dominates the resource budget of wearable sleep staging. Efficiency-oriented models fully observe a time period for decision, which is difficult to apply to resource-constrained actual deployment systems. Early time-series classification (ECTS) would stop once the observed prefix is decisive, but its instance-wise formulation assumes a stopping decision affects only the instance that made it. However, consecutive epochs are dependent, so stopping early truncates the history later epochs rely on, and a model trained on complete histories meets policy-induced covariate shift at inference. We introduce Prefix-Adaptive and Utility-Aware Stopping (PAUSE), a closed-loop framework in which each stopping decision controls both the current acquisition and the memory passed forward. A utility-aware finite-horizon stopper weighs stopping risk against expected continuation risk plus acquisition cost, a causal endpoint-memory backbone conditions each epoch on summaries taken at the policy-determined stopping points of its predecessors, and paired-history training combines complete and randomly truncated histories to reduce sensitivity to policy-induced history truncation. On four public datasets validation, PAUSE raises the macro-F1 versus acquisition frontier by 11.4 to 13.7 points over state-of-the-art ECTS baselines with 5.5 to 7.4 times less EEG. These results demonstrate the effectiveness of PAUSE for evidence-driven EEG acquisition in resource-constrained sleep monitoring.