用ECG和PPG信号个性化预测血压，跨场景准确可靠。
A Multi-scenario Attention-based Generative Model for Personalized Blood Pressure Time Series Forecasting
- 基于多场景注意力机制，融合ECG与PPG信号建模
- 在60名受试者、3种场景下均达AAMI标准
- 适合手术与重症监护中的高危患者实时监测
持续血压（BP）监测对危重症诊疗至关重要。然而个体间血压差异显著，亟需针对每位患者的生理特征构建个性化模型。本文提出一种基于心电图（ECG）与光电容积脉搏波（PPG）信号的个性化血压预测模型，采用二维表示学习捕捉复杂的生理关联。实验在三个不同场景下采集的60名受试者数据集上进行，结果表明该模型在跨场景条件下均满足美国医疗仪器协会（AAMI）标准，实现精准且鲁棒的血压预测。这种可靠的早期异常波动预警，对术中及重症监护中的高危患者具有重要意义，有助于降低死亡率并改善预后。
原文摘要 · Abstract (English)
Continuous blood pressure (BP) monitoring is essential for timely diagnosis and intervention in critical care settings. However, BP varies significantly across individuals, this inter-patient variability motivates the development of personalized models tailored to each patient's physiology. In this work, we propose a personalized BP forecasting model mainly using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. This time-series model incorporates 2D representation learning to capture complex physiological relationships. Experiments are conducted on datasets collected from three diverse scenarios with BP measurements from 60 subjects total. Results demonstrate that the model achieves accurate and robust BP forecasts across scenarios within the Association for the Advancement of Medical Instrumentation (AAMI) standard criteria. This reliable early detection of abnormal fluctuations in BP is crucial for at-risk patients undergoing surgery or intensive care. The proposed model provides a valuable addition for continuous BP tracking to reduce mortality and improve prognosis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。