AI 中文总结
研究对比直接PPG到BP预测与ECG介导管道来估计血压，通过对MIMIC - III数据库分析发现PPG与ABP耦合更强，经实验直接预测达A级性能优于ECG介导方法，为互联健康系统提供更优血压监测途径。
AI 中文摘要
连续无袖带血压监测对互联健康系统和可穿戴设备至关重要，能实现心血管疾病的早期检测、纵向跟踪和个性化管理。许多先前方法通过从光电容积脉搏波描记法（PPG）重建心电图（ECG）来间接估计血压，认为ECG与血压有更强生理联系。但本研究通过对MIMIC - III波形数据库进行大规模生理相关性分析发现，PPG与动脉血压（ABP）的耦合度（$|r| = 0.247$，$p < 0.001$）比ECG（$r = 0.018$，$p = 0.187$）更强。基于此，使用多种深度学习模型对直接PPG到BP预测和基于ECG介导的管道进行系统比较。在3127名患者的174万个片段上，直接PPG到BP预测达到英国高血压协会A级性能（收缩压平均绝对误差$\mathrm{MAE}_{\mathrm{SBP}} = 4.82 mmHg$，舒张压平均绝对误差$\mathrm{MAE}_{\mathrm{DBP}} = 4.31 mmHg$），优于所有基于ECG介导的方法（仅达到B级精度）。研究结果表明可直接从可穿戴PPG信号实现准确的连续血压监测，为实际互联健康系统提供更简单、高效的管道。
英文摘要
Continuous cuffless blood pressure (BP) monitoring is essential for connected health systems and wearable devices, enabling early detection, longitudinal tracking, and personalized management of cardiovascular disease. Many prior approaches attempt to estimate BP indirectly by reconstructing electrocardiography (ECG) from photoplethysmography (PPG), assuming ECG provides a stronger physiological link to BP. However, ECG sensing is less accessible in wearable settings and may introduce unnecessary complexity. In this work, we first perform a large-scale physiological correlation analysis on the MIMIC-III waveform database, revealing that PPG exhibits substantially stronger coupling with arterial blood pressure (ABP) ($|r|=0.247$, $p<0.001$) than ECG does ($r=0.018$, $p=0.187$), challenging the assumption that ECG provides a superior intermediate representation. Motivated by this insight, we conduct a systematic comparison between direct PPG-to-BP prediction and ECG-mediated pipelines using multiple state-of-the-art deep learning models. Across 1.74M segments from 3,127 patients, direct PPG-to-BP prediction achieves British Hypertension Society Grade A performance ($\mathrm{MAE}_{\mathrm{SBP}} = 4.82 mmHg$, $\mathrm{MAE}_{\mathrm{DBP}} = 4.31 mmHg$), outperforming all ECG-mediated approaches, which achieve only Grade B accuracy. Our findings suggest that accurate continuous BP monitoring can be achieved directly from wearable PPG signals, enabling simpler, more efficient pipelines for real-world connected health systems.
