Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human
hemodynamics, with recent studies highlighting its potential in assessing
vascular aging through deep learning. However, real-world age distributions are
often imbalanced, posing significant challenges for deep learning models. In
this paper, we introduce a novel, simple, and effective loss function named the
Dist Loss to address deep imbalanced regression tasks. We trained a
one-dimensional convolutional neural network (Net1D) incorporating the Dist
Loss on the extensive UK Biobank dataset (n=502,389) to estimate vascular age
from PPG signals and validate its efficacy in characterizing cardiovascular
health. The model's performance was validated on a 40% held-out test set,
achieving state-of-the-art results, especially in regions with small sample
sizes. Furthermore, we divided the population into three subgroups based on the
difference between predicted vascular age and chronological age: less than -10
years, between -10 and 10 years, and greater than 10 years. We analyzed the
relationship between predicted vascular age and several cardiovascular events
over a follow-up period of up to 10 years, including death, coronary heart
disease, and heart failure. Our results indicate that the predicted vascular
age has significant potential to reflect an individual's cardiovascular health
status. Our code will be available at this https URL

通过深度学习评估血管衰老表现的 Photoplethysmography (PPG) 成为监测人体血液动力学的关键工具，本文在实证研究中介绍了名为 Dist Loss 的新型简单有效的损失函数，以解决深度不平衡回归任务的挑战，并在大规模的 UK 生物银行数据集上训练了一维卷积神经网络（Net1D），使用 PPG 信号估计血管年龄，验证了其在刻画心血管健康方面的功效，结果表明预测的血管年龄能够显著反映个体的心血管健康状况。