Abstract
Photoplethysmogram (PPG) and electrocardiogram (ECG) are commonly recorded in intesive care unit (ICU) and operating room (OR). However, the high incidence of poor, incomplete, and inconsistent signal quality, can lead to false alarms or diagnostic inaccuracies. The methods explored so far suffer from limited generalizability, reliance on extensive labeled data, and poor cross-task transferability. To overcome these challenges, we introduce QualityFM, a novel multimodal foundation model for these physiological signals, designed to acquire a general-purpose understanding of signal quality. Our model is pre-trained on an large-scale dataset comprising over 21 million 30-second waveforms and 179,757 hours of data. Our approach involves a dual-track architecture that processes paired physiological signals of differing quality, leveraging a self-distillation strategy where an encoder for high-quality signals is used to guide the training of an encoder for low-quality signals. To efficiently handle long sequential signals and capture essential local quasi-periodic patterns, we integrate a windowed sparse attention mechanism within our Transformer-based model. Furthermore, a composite loss function, which combines direct distillation loss on encoder outputs with indirect reconstruction loss based on power and phase spectra, ensures the preservation of frequency-domain characteristics of the signals. We pre-train three models with varying parameter counts (9.6 M to 319 M) and demonstrate their efficacy and practical value through transfer learning on three distinct clinical tasks: false alarm of ventricular tachycardia detection, the identification of atrial fibrillation and the estimation of arterial blood pressure (ABP) from PPG and ECG signals.
Abstract (translated)
心电图（ECG）和光电容积描记图（PPG）是重症监护病房（ICU）和手术室（OR）中常见的记录生理信号。然而，这些信号往往出现质量差、不完整或一致性不佳的问题，这可能导致误报或诊断错误。迄今为止探索的方法存在泛化能力有限、依赖大量标注数据以及跨任务迁移性能较差等问题。为解决这些问题，我们引入了QualityFM，这是一种新型的多模态基础模型，专门针对生理信号的质量理解进行了设计。我们的模型在包含超过2100万份30秒波形和179,757小时数据的大规模数据集上进行了预训练。 我们的方法采用了一种双轨架构，用于处理质量不同的成对生理信号，并利用自蒸馏策略，即通过高质量信号的编码器来指导低质量信号的编码器的训练。为了高效地处理长序列信号并捕捉重要的局部准周期模式，我们在基于Transformer模型中集成了窗口稀疏注意力机制。 此外，我们采用了一种复合损失函数，该函数结合了直接在编码器输出上的蒸馏损失以及基于功率和相位谱的间接重建损失，以确保信号频域特性的保留。我们使用不同参数量（从9.6M到319M）预训练了三个模型，并通过三种不同的临床任务的迁移学习展示了其有效性和实用性：室性心动过速误报检测、心房颤动识别以及根据PPG和ECG信号估计动脉血压（ABP）。
URL
https://arxiv.org/abs/2509.06516
