QualityFM：一种面向危重患者信号质量挑战的基于自蒸馏的多模态生理信号基础模型
QualityFM: a Multimodal Physiological Signal Foundation Model with Self-Distillation for Signal Quality Challenges in Critically Ill Patients
AI总结：
提出多模态生理信号基础模型QualityFM，利用自蒸馏双轨架构与窗口稀疏注意力机制处理ICU中不同质量的PPG和ECG信号，在2100万波形上预训练并在三个临床任务上验证了其有效迁移性。
摘要：
光电容积脉搏波（PPG）和心电图（ECG）通常在重症监护病房（ICU）和手术室（OR）中记录。然而，信号质量差、不完整和不一致的高发生率，可能导致误报或诊断不准确。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能力差的问题。为了克服这些挑战，我们引入了QualityFM，一种针对这些生理信号的新型多模态基础模型，旨在获取对信号质量的通用理解。我们的模型在包含超过2100万个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。我们的方法采用双轨架构，处理不同质量的成对生理信号，利用自蒸馏策略，其中高质量信号的编码器用于指导低质量信号编码器的训练。为了高效处理长序列信号并捕获基本的局部准周期模式，我们在基于Transformer的模型中集成了窗口稀疏注意力机制。此外，结合编码器输出的直接蒸馏损失与基于功率和相位谱的间接重构损失的复合损失函数，确保了信号频域特征的保留。我们预训练了具有不同参数量（9.6 M至319 M）的三个模型，并通过在三个不同临床任务上的迁移学习展示了它们的有效性和实用价值：室性心动过速检测的误报、心房颤动的识别以及从PPG和ECG信号估算动脉血压（ABP）。
英文摘要：
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.
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