用图像化方法分析脉搏波，区分35-40岁与50-55岁健康人群。
Attractor Image-Based Deep Learning of Arterial Pulse Waves for Age Classification
- 将脉搏波信号转为图像，用卷积网络分类年龄组。
- 在内外部测试集上F1均超70%，表现稳定。
- 适合做可穿戴设备早期心血管风险筛查。
动脉脉搏波形态随年龄变化，反映心血管系统结构与功能改变。血管年龄是心血管健康的有效替代指标，过早老化提示疾病风险升高。脉搏波分析可用于无症状成人风险分层。本文将光电容积脉搏波（PPG）和动脉触诊信号的时间序列数据，通过对称投影吸引子重构（SPAR）方法转化为图像，并训练卷积神经网络，将健康受试者分为两个相近年龄组（35–40岁与50–55岁）。模型在内部及外部测试集上均表现出一致的分类性能，对PPG与触诊信号的F1分数均高于70%。结果表明，即使在年龄相近的健康人群中，SPAR生成的脉搏波图像仍包含可区分的形态特征。该概念验证研究为未来利用SPAR结合智能可穿戴设备进行早期风险检测奠定了基础。
原文摘要 · Abstract (English)
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.
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