无需标注数据，实时清除医疗脉搏信号中的伪影
Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Series
- 基于脉搏波特性设计无监督清洗框架，支持跨患者分布变化
- 在18万组动脉压数据上训练，跨疾病队列验证效果稳定
- 已集成至临床监测软件，适合真实场景下的连续生理信号处理
伪影会干扰临床对医疗时间序列的判断。脉搏波形提供了准确检测伪影的概率信息，但现有方法多依赖有监督学习，且忽视患者间分布差异。为此，我们提出通用无标签框架GenClean，用于实时伪影清洗，并基于自建的18万条十秒动脉血压（ABP）样本数据集进行训练。我们首先验证了该方法在同患者与跨患者分布偏移下的鲁棒性；进一步在MIMIC-III数据库上开展具有挑战性的跨疾病队列实验，确认其有效性；还将方法扩展至光电容积脉搏波（PPG），证明其适用于多种医疗脉搏信号。最终，将该框架集成至ICM+临床研究监测软件，证实其在连续生理监测中的实时可行性，为高分辨率医疗时间序列分析的可靠性提升奠定基础。
原文摘要 · Abstract (English)
Artefacts compromise clinical decision-making in the use of medical time series. Pulsatile waveforms offer probabilities for accurate artefact detection, yet most approaches rely on supervised manners and overlook patient-level distribution shifts. To address these issues, we introduce a generalised label-free framework, GenClean, for real-time artefact cleaning and leverage an in-house dataset of 180,000 ten-second arterial blood pressure (ABP) samples for training. We first investigate patient-level generalisation, demonstrating robust performances under both intra- and inter-patient distribution shifts. We further validate its effectiveness through challenging cross-disease cohort experiments on the MIMIC-III database. Additionally, we extend our method to photoplethysmography (PPG), highlighting its applicability to diverse medical pulsatile signals. Finally, its integration into ICM+, a clinical research monitoring software, confirms the real-time feasibility of our framework, emphasising its practical utility in continuous physiological monitoring. This work provides a foundational step toward precision medicine in improving the reliability of high-resolution medical time series analysis
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