发表机构
University of New Brunswick（新不伦瑞克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ExpertoRhythm利用注意力增强的一维U-Net从单通道PPG重建ABP波形，通过形态感知复合损失提升血压估计精度，在942名受试者上实现SBP/DBP MAE为2.46/1.46 mmHg，满足AAMI标准并达BHS A级。
AI 中文摘要
基于光电容积脉搏波（PPG）的连续无袖带血压（BP）监测在可穿戴健康和远程监护中具有巨大潜力，但由于PPG到BP的映射必须保留细微的波形形态和依赖于压力范围的动态特性，准确估计仍然困难。我们提出了ExpertoRhythm，一种注意力增强的一维U-Net，可从单通道PPG信号重建动脉血压（ABP）波形，并直接从重建波形中导出收缩压和舒张压。核心贡献是复合形态感知学习目标，该目标将范围加权SmoothL1重建与窗口范围正则化相结合，以强调高动态血压段并减少幅度过冲/欠冲。在包含942名受试者的UCI无袖带血压数据集上，ExpertoRhythm在收缩压/舒张压（SBP/DBP）上实现了2.46/1.46 mmHg的平均绝对误差（MAE），同时在波形重建和血压估计指标上，相对于纯MSE平均相对误差降低了30.4%。临床风格评估进一步显示，在整个血压范围内（包括高达200 mmHg的高压窗口）具有低偏差和强一致性，满足AAMI标准并达到BHS A级。这些结果表明，从单通道PPG进行形态感知波形重建可为可穿戴和远程护理环境中的连续无袖带血压监测提供准确且实用的途径。
英文摘要
Continuous cuffless blood pressure (BP) monitoring from photoplethysmography (PPG) has strong potential for wearable health and telemonitoring, but accurate estimation remains difficult because PPG-to-BP mapping must preserve subtle waveform morphology and pressure-range-dependent dynamics. We introduce ExpertoRhythm, an attention-enhanced 1D U-Net that reconstructs the arterial blood pressure (ABP) waveform from a single-channel PPG signal and derives systolic and diastolic BP directly from the reconstructed waveform. The central contribution is a composite morphology-aware learning objective that integrates range-weighted SmoothL1 reconstruction with a window-range regularizer to emphasize high-dynamic BP segments and reduce amplitude under/over-shoot. On the UCI cuff-less BP dataset with 942 subjects, ExpertoRhythm achieves 2.46/1.46 mmHg MAE for systolic/diastolic BP (SBP/DBP), while obtaining a 30.4% average relative error reduction over pure MSE across waveform reconstruction and BP estimation metrics. Clinical-style evaluation further demonstrates low bias and strong agreement across the BP range, including high-pressure windows up to 200 mmHg, satisfying AAMI criteria and achieving BHS Grade A. These results suggest that morphology-aware waveform reconstruction from a single PPG channel can provide an accurate and practical pathway toward continuous cuffless BP monitoring in wearable and remote-care settings.
