Tang Qingfeng, Ding Pengcheng, Dai Guowei, Zhang Liangliang, Wang Guangjun, Su Benyue, Hu Xiaojuan, Cui Ji, Qu Haoyu, An Hui
Digital and Intelligent Health Research Center, Anqing Normal University, Anqing, 246133, China.
School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
Sci Data. 2025 Jul 22;12(1):1274. doi: 10.1038/s41597-025-05598-1.
Early assessment of cardiovascular disease risk plays an important role in preventing cardiovascular disease, vascular age (VA) is an important indicator for early screening of cardiovascular disease risk. This study presents a pulse signal-based dataset for VA prediction. The dataset comprises 226 subjects with 1364 pulse cycles, spanning both sexes (49.6% male, 50.4% female) and an age range of 20 to 69 years. Pulse signals were denoised by Savitzky-Golay filters, and 4th-order derivatives were calculated to extract the features of pulse signal. We applied the classic statistical model Klemera Doubal method (KDM) and five artificial intelligence models to predict VA. The experimental results showed that these models can predict VA with high accuracy and stability. It indicates that using pulse signals to predict VA is a simple, non-invasive, and effective method for assessing vascular health.
心血管疾病风险的早期评估在预防心血管疾病中起着重要作用，血管年龄（VA）是早期筛查心血管疾病风险的重要指标。本研究提出了一个基于脉搏信号的血管年龄预测数据集。该数据集包含226名受试者的1364个脉搏周期，涵盖了不同性别（男性49.6%，女性50.4%），年龄范围为20至69岁。脉搏信号通过Savitzky-Golay滤波器进行去噪，并计算其四阶导数以提取脉搏信号特征。我们应用经典统计模型克莱梅拉·杜巴尔方法（KDM）和五种人工智能模型来预测血管年龄。实验结果表明，这些模型能够高精度、稳定地预测血管年龄。这表明利用脉搏信号预测血管年龄是一种简单、无创且有效的血管健康评估方法。
