发表机构
King’s College London; Ghent University; National Physical Laboratory; University of Surrey; Ghent University Hospital（伦敦国王学院; 根特大学; 英国国家物理实验室; 萨里大学; 根特大学医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将脉搏波时间序列转化为SPAR图像，训练卷积神经网络分类年龄相近的健康人群，F1分数超70%，为智能可穿戴早期风险检测奠定基础。
AI 中文摘要
动脉脉搏波的形态随年龄变化，反映心血管系统的结构与功能改变，因此血管年龄是评估心血管健康的重要替代标志物，过早血管老化预示疾病风险升高。脉搏波分析可用于对无症状成年人进行风险分层。本研究采用对称投影吸引子重建（Symmetric Projection Attractor Reconstruction, SPAR）方法，将光体积描记法（Photoplethysmography, PPG）和动脉张力测量的脉搏波时间序列数据转化为图像，利用这些SPAR图像训练卷积神经网络，将健康受试者分为两个年龄相近的组（35-40岁和50-55岁）。该模型在内部和外部测试集上表现出一致的分类性能，针对PPG和张力测量信号的F1分数均超过70%。结果表明，SPAR衍生的脉搏波图像包含年龄相近健康受试者的判别性形态特征，该概念验证为未来利用SPAR结合智能可穿戴设备进行早期风险检测的研究奠定了基础。
英文摘要
Arterial pulse waveform morphology evolves with age, reflecting structural and functional changes in the cardiovascular system. Thus, vascular age is a valuable surrogate marker of cardiovascular health, and premature vascular ageing can indicate increased disease risk. Pulse wave analysis could support risk stratification in otherwise asymptomatic adults. We transformed pulse wave time-series data from photoplethysmography (PPG) and arterial tonometry into images, using the Symmetric Projection Attractor Reconstruction (SPAR) method. These SPAR images were used to train a convolutional neural network to classify healthy subjects into two closely spaced age groups (35-40 and 50-55 years). The model demonstrated consistent classification performance across internal and external test sets, achieving F1 scores above 70% for both PPG and tonometry signals. These results suggest that SPAR-derived pulse wave images contain discriminative morphological features even among healthy adults close in age. This proof-of-concept lays the groundwork for future research into the use of SPAR for early risk detection using smart wearables.
CommentsAccepted at Computing in Cardiology 2025, published in conference proceedings. 8 pages, 2 figures
