Multi-Window Temporal Context for ECG-Based Sleep Apnea Detection Under Limited Apnea-Label Availability

Background/Objectives: Supervised electrocardiography-based sleep apnea detection often depends on dense segment-level annotations, creating substantial expert burden. To reduce this dependence, we investigated whether adjacent temporal context could improve classification when ground-truth apnea labels are limited. Methods: We systematically evaluated five context lengths and seven apnea-label retention levels across 35 experimental configurations using participant-grouped five-fold cross-validation with three training repetitions. Results: The results demonstrated that using multi-window temporal context consistently improved classification performance across the evaluated apnea-label retention range. All multi-window configurations achieved higher mean F1-scores than the corresponding single-window baseline, with the best-performing multi-window setting at each retention level providing gains of 7.18–12.23 percentage points. Context length showed a clear overall effect on performance, whereas no systematic interaction was observed between context length and apnea-label retention. Targeted repeated-center and random-shuffle control experiments further supported a contribution from temporally adjacent ECG information rather than sequence-length expansion, repeated target presentation, or temporally distant same-recording context. Conclusions: Overall, leveraging adjacent temporal context provides a practical strategy for improving ECG-based apnea classification and may be particularly useful for developing screening models when dense expert-provided apnea annotations are limited.