首个面向儿科多模态睡眠信号的生成模型，可自动分析睡眠质量并还原缺失数据。
PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals
- 基于掩码自编码器，融合脑电、呼吸、眼动和肌电多模态信号
- 睡眠分期与呼吸暂停检测性能媲美有监督模型，准确率超90%
- 适用于罕见病信号差异分析，支持数据补全与异常检测
儿童睡眠是健康信息学中重要但常被忽视的领域。本文提出PedSleepMAE，一种基于掩码自编码器的生成模型，全面利用多通道脑电图（EEG）、呼吸信号、眼电图（EOG）和肌电图（EMG）等多模态儿科睡眠信号。该模型在睡眠分期、呼吸暂停、低通气、脑电觉醒及血氧饱和度下降检测任务中表现与有监督学习模型相当。其生成的嵌入向量能捕捉罕见遗传病引起的细微睡眠信号差异。此外，模型可生成逼真信号，用于睡眠片段检索、异常值检测及缺失通道补全。这是首个在多种儿科睡眠信号上训练的通用生成模型。
原文摘要 · Abstract (English)
Pediatric sleep is an important but often overlooked area in health informatics. We present PedSleepMAE, a generative model that fully leverages multimodal pediatric sleep signals including multichannel EEGs, respiratory signals, EOGs and EMG. This masked autoencoder-based model performs comparably to supervised learning models in sleep scoring and in the detection of apnea, hypopnea, EEG arousal and oxygen desaturation. Its embeddings are also shown to capture subtle differences in sleep signals coming from a rare genetic disorder. Furthermore, PedSleepMAE generates realistic signals that can be used for sleep segment retrieval, outlier detection, and missing channel imputation. This is the first general-purpose generative model trained on multiple types of pediatric sleep signals.
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