Department of Biomedical Engineering, King's College London, London, United Kingdom.
Department of Clinical Pharmacology, St. Thomas' Hospital, King's College London, London, United Kingdom.
PLoS One. 2021 Jun 28;16(6):e0245026. doi: 10.1371/journal.pone.0245026. eCollection 2021.
One of the European gold standard measurement of vascular ageing, a risk factor for cardiovascular disease, is the carotid-femoral pulse wave velocity (cfPWV), which requires an experienced operator to measure pulse waves at two sites. In this work, two machine learning pipelines were proposed to estimate cfPWV from the peripheral pulse wave measured at a single site, the radial pressure wave measured by applanation tonometry. The study populations were the Twins UK cohort containing 3,082 subjects aged from 18 to 110 years, and a database containing 4,374 virtual subjects aged from 25 to 75 years. The first pipeline uses Gaussian process regression to estimate cfPWV from features extracted from the radial pressure wave using pulse wave analysis. The mean difference and upper and lower limits of agreement (LOA) of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.2 m/s, and 3.75 m/s & -3.34 m/s, respectively. The second pipeline uses a recurrent neural network (RNN) to estimate cfPWV from the entire radial pressure wave. The mean difference and upper and lower LOA of the estimation on the 924 hold-out test subjects from the Twins UK cohort were 0.05 m/s, and 3.21 m/s & -3.11m/s, respectively. The percentage error of the RNN estimates on the virtual subjects increased by less than 2% when adding 20% of random noise to the pressure waveform. These results show the possibility of assessing the vascular ageing using a single peripheral pulse wave (e.g. the radial pressure wave), instead of cfPWV. The proposed code for the machine learning pipelines is available from the following online depository (https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal).
作为心血管疾病风险因素的血管老化的欧洲金标准测量之一，是颈-股脉搏波速度(cfPWV)，这需要有经验的操作人员在两个部位测量脉搏波。在这项工作中，提出了两种机器学习管道，从单个部位测量的外周脉搏波（通过平板测压法测量的桡动脉压力波）估计 cfPWV。研究人群是包含 3082 名年龄在 18 至 110 岁的英国双胞胎队列，以及一个包含 4374 名年龄在 25 至 75 岁的虚拟受试者的数据库。第一管道使用高斯过程回归，从使用脉搏波分析从桡动脉压力波中提取的特征估计 cfPWV。在英国双胞胎队列的 924 名外部测试受试者中，估计的平均差值和上下一致性界限（LOA）分别为 0.2 m/s 和 3.75 m/s 和-3.34 m/s。第二管道使用递归神经网络（RNN）从整个桡动脉压力波估计 cfPWV。在英国双胞胎队列的 924 名外部测试受试者中，估计的平均差值和上下 LOA 分别为 0.05 m/s 和 3.21 m/s 和-3.11m/s。当将压力波形中的随机噪声增加 20%时，RNN 估计的虚拟受试者的误差百分比增加不到 2%。这些结果表明，使用单个外周脉搏波（例如桡动脉压力波）而不是 cfPWV 评估血管老化是可能的。用于机器学习管道的代码可从以下在线存储库获得（https://github.com/WeiweiJin/Estimate-Cardiovascular-Risk-from-Pulse-Wave-Signal）。
