用 SPAR 把脉搏波变成图像，再让 CNN 分年龄，35-40 和 50-55 岁分得挺准，F1 超 70%。
研究人员用 SPAR 方法将光电容积脉搏波（PPG）和动脉张力测量得到的脉搏波时间序列转换为图像，再用卷积神经网络分类健康受试者的年龄。模型在内部和外部测试集上表现一致，对 PPG 和张力测量信号的 F1 分数均超过 70%。该模型能区分 35-40 岁和 50-55 岁两个相近年龄组，说明 SPAR 图像保留了健康成年人中随年龄变化的形态特征。
Attractor Image-Based Deep Learning of Arterial Pulse Waves for Age Classification
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.