MorphologyFM：一种用于从心电图和脉搏血氧波形中进行形态感知表示学习的基础模型
MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms
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中文总结 AI 辅助
研究针对现有生理波形方法未保留临床意义波形形态的问题，提出多模态基础模型MorphologyFM，通过形态感知自监督学习目标预训练，结合多种技术学习相关表示，在多下游任务中表现优于其他方法，证明联合建模更具可转移性。
中文摘要 AI 辅助
基础模型已成为从大规模生物医学数据中学习可转移表示的强大范式，但现有的生理波形方法主要优化重建或预测目标，未明确保留具有临床意义的波形形态。心电图（ECG）和脉搏血氧（SpO2）波形通过其形态结构编码丰富的心血管和血流动力学信息。本文介绍了MorphologyFM，这是一种多模态基础模型，使用形态感知自监督学习目标在来自MIMIC重症监护数据库的配对ECG和SpO2波形上进行预训练。它结合了形态引导掩蔽、跨模态表示学习和对比潜在对齐，以学习捕获临床相关生理结构的表示，无需人工注释。在多个下游预测任务中评估了MorphologyFM，包括心律失常分类、低氧血症预测、死亡率预测和住院时间估计，结果表明其优于代表性的自监督学习方法。此外，联合建模ECG和SpO2波形产生的可转移表示比单模态预训练更多。研究结果确立了波形形态作为自监督生理表示学习的强大归纳偏差，并引入了MorphologyFM作为连续生理监测的通用基础模型。
英文摘要
Foundation models have recently emerged as a powerful paradigm for learning transferable representations from large scale biomedical data, yet existing approaches for physiological waveforms primarily optimize reconstruction or forecasting objectives that do not explicitly preserve clinically meaningful waveform morphology. Electrocardiograms (ECGs) and pulse oximetry (SpO2) waveforms encode rich cardiovascular and hemodynamic information through their morphological structure. In this work, we introduce MorphologyFM, a multimodal foundation model pretrained on paired ECG and SpO2 waveforms from the MIMIC critical care database using a morphology aware self supervised learning objective. MorphologyFM combines morphology guided masking, cross modal representation learning, and contrastive latent alignment to learn representations that capture clinically relevant physiological structure without requiring manual annotations. We evaluate MorphologyFM across multiple downstream prediction tasks, including arrhythmia classification, hypoxemia prediction, mortality prediction, and length of stay estimation, demonstrating consistent improvements over representative self supervised learning methods, including Masked Autoencoders (MAE), contrastive learning, Barlow Twins, and Joint Embedding Predictive Architectures (JEPA). Furthermore, we show that jointly modeling ECG and SpO2 waveforms produces more transferable representations than single modality pretraining. Our results establish waveform morphology as a powerful inductive bias for self supervised physiological representation learning and introduce MorphologyFM as a general purpose foundation model for continuous physiological monitoring.
发表机构
- University of the Chinese Academy of Sciences（中国科学院大学）
- Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
