Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan; Biomedical Engineering Research Center, National Defense Medical Center, Taipei, Taiwan.
Division of Cardiology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, 23561 New Taipei City, Taiwan; Taipei Heart Institute, Taipei Medical University, Taipei, Taiwan; Division of Cardiology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Microvasc Res. 2022 Jan;139:104240. doi: 10.1016/j.mvr.2021.104240. Epub 2021 Sep 8.
Aging contributes to the progression of vascular dysfunction and is a major nonreversible risk factor for cardiovascular disease. The aim of this study was to determine the effectiveness of using arterial pulse-wave measurements, frequency-domain pulse analysis, and machine-learning analysis in distinguishing vascular aging. Radial pulse signals were measured noninvasively for 3 min in 280 subjects aged 40-80 years. The cardio-ankle vascular index (CAVI) was used to evaluate the arterial stiffness of the subjects. Forty frequency-domain pulse indices were used as features, comprising amplitude proportion (C), coefficient of variation of C, phase angle (P), and standard deviation of P (n = 1-10). Multilayer perceptron and random forest with supervised learning were used to classify the data. The detected differences were more prominent in the subjects aged 40-50 years. Several indices differed significantly between the non-vascular-aging group (aged 40-50 years; CAVI <9) and the vascular-aging group (aged 70-80 years). Fivefold cross-validation revealed an excellent ability to discriminate the two groups (the accuracy was >80%, and the AUC was >0.8). For subjects aged 50-60 and 60-70 years, the detection accuracies of the two trained algorithms were around 80%, with AUCs of >0.73 for both, which indicated acceptable discrimination. The present method of frequency-domain analysis may improve the index reliability for further machine-learning analyses of the pulse waveform. The present noninvasive and objective methodology may be meaningful for developing a wearable-device system to reduce the threat of vascular dysfunction induced by vascular aging.
衰老是血管功能障碍进展的一个主要因素，也是心血管疾病的一个主要不可逆转的风险因素。本研究旨在确定使用动脉脉搏波测量、频域脉搏分析和机器学习分析来区分血管老化的有效性。对 280 名年龄在 40-80 岁的受试者进行了 3 分钟的无创桡动脉脉搏信号测量。使用心血管踝动脉指数（CAVI）评估受试者的动脉僵硬程度。40 个频域脉搏指数作为特征，包括幅度比（C）、C 的变异系数、相位角（P）和 P 的标准差（n=1-10）。采用多层感知器和随机森林监督学习对数据进行分类。在 40-50 岁的受试者中，检测到的差异更为明显。在非血管老化组（年龄 40-50 岁；CAVI<9）和血管老化组（年龄 70-80 岁）之间，有几个指数差异显著。五折交叉验证显示出极好的区分两组的能力（准确率>80%，AUC>0.8）。对于年龄在 50-60 岁和 60-70 岁的受试者，两种训练算法的检测准确率均在 80%左右，AUC 均大于 0.73，表明具有可接受的区分能力。频域分析方法可能会提高脉搏波指数的可靠性，有助于进一步进行脉搏波的机器学习分析。这种非侵入性和客观的方法对于开发可穿戴设备系统以降低血管老化引起的血管功能障碍的威胁可能具有重要意义。
