arXiv cs.AI· Cheol-Hui Lee, Wookyung Kim, Hyeonhoon Lee, Garam Lee, Da-in Eun, Hyung-Chul Lee·· 1 天前AI 评分38
CARMEN：基于十种心肺生理信号预训练的基础模型，用于围术期与重症监护
A Cardiorespiratory Foundation Model Pretrained on Ten Physiological Signals for Perioperative and Critical Care
AI 导读
CARMEN 是一个在十种心肺波形（2.27 百万模态小时）上无标签预训练的单编码器基础模型，可接受 10 秒至 5 分钟任意共录信号组合。其 LSCNorm 条件机制恢复了归一化去除的绝对信号水平，缺失该机制时术中低血压 AUROC 从 0.894 降至 0.780。
正文 · 原文
Abstract:Arterial, venous, pulmonary artery and intracranial pressures and ventilator waveforms guide decisions in operating rooms and intensive care units, yet foundation models for physiological waveforms have been built almost entirely on the electrocardiogram and photoplethysmogram. We present CARMEN, a single encoder pretrained without labels on ten cardiorespiratory waveforms (2.27 million modality-hours). CARMEN accepts any combination of co-recorded signals from 10 s to 5 min, and a conditioning mechanism, LSCNorm, restores the absolute signal level that normalization removes; without it, the AUROC for intraoperative hypotension fell from 0.894 to 0.780. Across 19 tasks from Korean and US cohorts, the frozen encoder outperformed supervised models on 12 of 15 and three foundation models, one general-purpose and two for biosignals, on 14 to 17 of 19, with none exceeding it on any task. Given the electrocardiogram or photoplethysmogram alone, it matched or exceeded signal-specific foundation models in most comparisons, and it retained its performance when channels were missing.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15004 [cs.LG] |
| (or arXiv:2611.15004v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15004 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Cheol-Hui Lee [view email]
[v1]
Wed, 11 Nov 2026 04:02:50 UTC (10,396 KB)
来源：arXiv cs.AI · arxiv.org