Abstract
Cardiac biosignals, such as electrocardiograms (ECG) and photoplethysmograms (PPG), are of paramount importance for the diagnosis, prevention, and management of cardiovascular diseases, and have been extensively used in a variety of clinical tasks. Conventional deep learning approaches for analyzing these signals typically rely on homogeneous datasets and static bespoke models, limiting their robustness and generalizability across diverse clinical settings and acquisition protocols. In this study, we present a cardiac sensing foundation model (CSFM) that leverages advanced transformer architectures and a generative, masked pretraining strategy to learn unified representations from vast, heterogeneous health records. Our model is pretrained on an innovative multi-modal integration of data from multiple large-scale datasets (including MIMIC-III-WDB, MIMIC-IV-ECG, and CODE), comprising cardiac signals and the corresponding clinical or machine-generated text reports from approximately 1.7 million individuals. We demonstrate that the embeddings derived from our CSFM not only serve as effective feature extractors across diverse cardiac sensing scenarios, but also enable seamless transfer learning across varying input configurations and sensor modalities. Extensive evaluations across diagnostic tasks, demographic information recognition, vital sign measurement, clinical outcome prediction, and ECG question answering reveal that CSFM consistently outperforms traditional one-modal-one-task approaches. Notably, CSFM exhibits robust performance across multiple ECG lead configurations from standard 12-lead systems to single-lead setups, and in scenarios where only ECG, only PPG, or a combination thereof is available. These findings highlight the potential of CSFM as a versatile and scalable solution, for comprehensive cardiac monitoring.
Abstract (translated)
心脏生物信号，如心电图（ECG）和光电容积描记图（PPG），对于诊断、预防和管理心血管疾病至关重要，并已在各种临床任务中广泛应用。传统的深度学习方法在分析这些信号时通常依赖于同质的数据集和静态定制模型，这限制了它们在不同临床环境和采集协议下的稳健性和泛化能力。 在这项研究中，我们提出了一个心脏感知基础模型（CSFM），它利用先进的转换器架构和生成式、屏蔽预训练策略从庞大的异构健康记录中学习统一的表示。我们的模型是通过将来自多个大规模数据集（包括MIMIC-III-WDB、MIMIC-IV-ECG 和 CODE）的数据进行创新的多模态整合来预先训练的，这些数据集包含了大约170万个体的心脏信号及其相应的临床或机器生成的文本报告。我们证明了从我们的CSFM 中导出的嵌入不仅可以作为在各种心脏感知场景中的有效特征提取器，还能够实现在不同输入配置和传感器模式下的无缝迁移学习。 通过诊断任务、人口统计信息识别、生命体征测量、临床结果预测以及心电图问题回答等多方面的广泛评估显示，CSFM 在传统的单一模态单一任务方法上表现出了持续的优越性。值得注意的是，在标准12导联系统到单导联设置等各种ECG 导联配置中，CSFM 表现出稳健的表现，并且在仅提供 ECG 信号、PPG 信号或两者组合的情况下也能保持良好的性能。 这些发现突显了 CSFM 作为一种灵活和可扩展的解决方案的潜力，以实现全面的心脏监测。
URL
https://arxiv.org/abs/2507.01045
