用呼吸信号生成睡眠脑电图，实现无接触神经评估
Physiology as Language: Translating Respiration to Sleep EEG
- 基于波形条件生成框架，保留呼吸细节并约束脑电空间
- 在2.8万多人数据上实现7%的脑电图谱重构误差
- 可支持年龄、性别、睡眠分期等下游任务，适合远程睡眠监测
本文提出一种跨生理信号翻译新任务：从呼吸信号合成睡眠脑电图（EEG）。为应对两种模态间显著的复杂性差异，我们设计了一种波形条件生成框架，既保留精细呼吸动态，又通过离散标记化约束目标脑电空间。模型在超过2.8万名个体的数据上训练，实现了7%的脑电图谱重构均方绝对误差。除重建外，合成的脑电图在年龄估计（MAE 5.0年 vs. 5.1年）、性别分类（AUROC 0.81 vs. 0.82）和睡眠分期（准确率0.84 vs. 0.88）等下游任务中表现接近真实脑电图，显著优于直接使用呼吸信号训练的基线模型。最后，我们证明该框架可推广至非接触式传感，通过无线射频反射合成脑电图，验证了睡眠期间远程无接触神经评估的可行性。
原文摘要 · Abstract (English)
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.
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