无需穿戴设备，睡觉时通过床面传感器监测心脏健康。
BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing
- 用床垫传感器采集无感心电波形，大规模预训练建模。
- 生物年龄预测误差仅3.26年，优于现有非接触式方法。
- 仅需500人标注数据就超全监督模型，适合长期健康监测。
可穿戴生物信号基础模型已在多项临床任务中达到或超越监督型专用模型性能，但均依赖用户主动佩戴设备或前往睡眠实验室。我们提出BCG-FM，首个面向环境机械生物信号的基础模型。嵌入床面的压电传感器在夜间无感记录球心动图（BCG）；我们基于145,985名个体总计275万小时的夜间数据，采用个体级对比学习进行预训练，构建迄今最大的原始波形生物信号预训练语料库。冻结的BCG-FM嵌入在生物年龄估计上达到3.26年的平均绝对误差（最低记录于任何环境非接触模态），并在15种自报健康状况及三个独立外部队列中实现临床相关区分能力。仅用500名标注参与者预训练的表示即优于使用3,372名样本全监督训练的基线模型，且表征质量随对比批大小呈对数线性增长。这些结果确立了环境化、长期性的机械生物信号作为健康基础模型的可行模态。
原文摘要 · Abstract (English)
Foundation models for wearable biosignals have matched or exceeded supervised specialists across a range of clinical tasks, yet all rely on modalities that require deliberate user action--wearing a device or visiting a sleep lab. We introduce BCG-FM, the first foundation model for ambient mechanical biosignals. A piezoelectric sensor embedded in the bed surface records ballistocardiography (BCG) each night without user effort; we pretrain BCG-FM with participant-level contrastive learning and using a total of 2.75 million hours of nightly recordings from 145,985 individuals, the largest raw-waveform biosignal pretraining corpus to date. Frozen BCG-FM embeddings achieve 3.26-year MAE on biological-age estimation (the lowest reported for any ambient, contactless modality) and yield clinically relevant discrimination across 15 self-reported health conditions and three independent external cohorts. Pretrained representations from only 500 labeled participants outperform a fully supervised baseline trained on 3,372, and representation quality scales log-linearly with contrastive batch size. These results establish ambient, longitudinal mechanical biosignals as a viable modality for health foundation models.
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