生理学作为语言：将呼吸转化为睡眠EEG
Physiology as Language: Translating Respiration to Sleep EEG
AI总结：
本文提出了一种从呼吸信号合成睡眠EEG的跨生理翻译框架，实现了EEG重建及下游任务的高精度表现，展示了远程非接触神经评估的可行性。
摘要：
本文介绍了一种新的跨生理翻译任务：从呼吸信号合成睡眠脑电图（EEG）。为了解决两种模态之间的显著复杂性差距，我们提出了一种波形条件生成框架，该框架在保留细粒度呼吸动态的同时，通过离散标记化约束EEG目标空间。在超过28,000名个体上训练，我们的模型在EEG频谱图重建中实现了7%的平均绝对误差。除了重建外，合成的EEG在年龄估计（MAE 5.0 vs. 5.1年）、性别检测（AUROC 0.81 vs. 0.82）和睡眠分期（准确率0.84 vs. 0.88）等下游任务中表现与真实EEG相当，显著优于直接在呼吸上训练的基线。最后，我们证明该框架可以推广到无接触传感，通过无线射频反射合成EEG，突显了在睡眠期间进行远程、非接触式神经评估的可行性。
英文摘要：
This paper introduces a novel cross-physiology translation task: synthesizing sleep electroencephalography (EEG) from respiration signals. To address the significant complexity gap between the two modalities, we propose a waveform-conditional generative framework that preserves fine-grained respiratory dynamics while constraining the EEG target space through discrete tokenization. Trained on over 28,000 individuals, our model achieves a 7% Mean Absolute Error in EEG spectrogram reconstruction. Beyond reconstruction, the synthesized EEG supports downstream tasks with performance comparable to ground truth EEG on age estimation (MAE 5.0 vs. 5.1 years), sex detection (AUROC 0.81 vs. 0.82), and sleep staging (Accuracy 0.84 vs. 0.88), significantly outperforming baselines trained directly on breathing. Finally, we demonstrate that the framework generalizes to contactless sensing by synthesizing EEG from wireless radio-frequency reflections, highlighting the feasibility of remote, non-contact neurological assessment during sleep.
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。
