BiTimeCrossNet：面向儿童睡眠的时序自监督学习
BiTimeCrossNet: Time-Aware Self-Supervised Learning for Pediatric Sleep
AI总结：
BiTimeCrossNet通过引入时间信息和交叉注意力机制，在儿童睡眠分析中实现更高效的多模态自监督学习。
摘要：
我们提出了BiTimeCrossNet（BTCNet），一种用于长时间生理记录（如整夜睡眠研究）的多模态自监督学习框架。虽然许多现有方法在短片段上进行训练，将每个片段视为独立样本，但BTCNet将每个片段在父记录中的时间信息纳入考虑，例如在睡眠会话中。BTCNet进一步通过交叉注意力机制学习生理信号之间的成对交互，无需任务标签或序列级监督。我们对六种下游任务进行了BTCNet的评估，包括睡眠分期、觉醒检测和呼吸事件检测。在冻结骨干线性探测下，BTCNet在与其他相同但非时间感知的变体相比，始终表现出色，其优势在独立的儿童数据集上也得以泛化。与现有多模态自监督睡眠模型相比，BTCNet在呼吸相关任务上表现尤为突出。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
