IEEE Trans Biomed Eng. 2018 Dec;65(12):2742-2750. doi: 10.1109/TBME.2018.2814630. Epub 2018 Mar 9.
Vascular ageing is known to be accompanied by arterial stiffening and vascular endothelial dysfunction, and represents an independent factor contributing to the development of cardiovascular disease. The microvascular pulse is affected by the biomechanical alterations of the circulatory system, and has been the focus of studies aiming at the development of non-invasive methods able to extract physiologically relevant features.
proposing an approach for the assessment of vascular ageing based on a support vector machine (SVM) learning from features of the pulse contour.
the supervised classifier was trained and validated over 20935 models of pulse wave, obtained with a multi-Gaussian decomposition algorithm, applied to laser Doppler flowmetry signals of 54 healthy, non-smoker subjects.
the multi-Gaussian model showed a mean R of 0.98 and an average normalized root mean square error of 0.90, demonstrating the ability to reconstruct the pulse shape. Over 30 training and validation experiments, the SVM showed a mean Pearson's r of 0.808 between the rate of waves classified as old and the age of the subjects, along with an average area under the ROC curve of 0.953.
the SVM showed the capability to discriminate differently aged individuals.
the proposed method might detect the ageing-related modifications of the vascular tree; furthermore, since diabetes promotes vascular alterations comparable to ageing, this approach may be also suitable for the screening of diabetic angiopathy.
目的：提出一种基于支持向量机（SVM）从脉搏轮廓特征中学习的血管老化评估方法。
方法：该监督分类器在经过多高斯分解算法处理的 54 名健康不吸烟者的激光多普勒血流信号上，对 20935 个脉搏波模型进行了训练和验证。
结论：SVM 能够区分不同年龄的个体。
意义：该方法可能检测到与血管老化相关的变化；此外，由于糖尿病引起的血管改变与老化相似，因此这种方法也可能适用于糖尿病血管病变的筛查。
