BTCNet通过时间感知自监督学习，提升儿童睡眠信号分析精度。
BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleep
- 引入时间上下文信息，建模生理信号片段在完整睡眠中的位置关系。
- 跨注意力机制捕捉多模态信号间配对交互，无需标签或序列级监督。
- 在6项下游任务中表现优异，尤其在呼吸事件检测上优势明显。
我们提出BiTimeCrossNet（BTCNet），一种用于长时生理记录（如整夜睡眠研究）的多模态自监督学习框架。现有方法通常将短片段视为独立样本进行训练，而BTCNet引入了每个片段在其父记录（如睡眠会话）中的发生时间信息。BTCNet通过交叉注意力机制学习不同生理信号间的成对交互，无需任务标签或序列级监督。我们在六项儿科睡眠下游任务上评估BTCNet，包括睡眠分期、觉醒检测和呼吸事件检测。在冻结主干的线性探测下，BTCNet始终优于无时间感知的对照模型，且在独立儿科数据集上性能仍具泛化能力。相较于现有多模态自监督睡眠模型，BTCNet表现强劲，尤其在呼吸相关任务上领先。
原文摘要 · Abstract (English)
We present BiTimeCrossNet (BTCNet), a multimodal self-supervised learning framework for long physiological recordings such as overnight sleep studies. While many existing approaches train on short segments treated as independent samples, BTCNet incorporates information about when each segment occurs within its parent recording, for example within a sleep session. BTCNet further learns pairwise interactions between physiological signals via cross-attention, without requiring task labels or sequence-level supervision. We evaluate BTCNet on pediatric sleep data across six downstream tasks, including sleep staging, arousal detection, and respiratory event detection. Under frozen-backbone linear probing, BTCNet consistently outperforms an otherwise identical non-time-aware variant, with gains that generalize to an independent pediatric dataset. Compared to existing multimodal self-supervised sleep models, BTCNet achieves strong performance, particularly on respiration-related tasks.
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