{"title":"基于腕部脉搏信号的血管年龄计算，采用混合高斯模型和支持向量回归。","authors":"Qingfeng Tang, Shoujiang Xu, Mengjuan Guo, Guangjun Wang, Zhigeng Pan, Benyue Su","doi":"10.1007/s13755-022-00172-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal.</p><p><strong>Methods: </strong>Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices.</p><p><strong>Results: </strong>Compared with the CA, the VA is closer to PW (<i>r</i> = 0.539, <i>P</i> < 0.001 to <i>r</i> = 0.589, <i>P</i> < 0.001 in men; <i>r</i> = 0.325, <i>P</i> < 0.001 to <i>r</i> = 0.400, <i>P</i> < 0.001 in women), IPA (<i>r</i> = - 0.446, <i>P</i> < 0.001 to <i>r</i> = - 0.534, <i>P</i> < 0.001 in men; <i>r</i> = - 0.623, <i>P</i> < 0.001 to <i>r</i> = - 0.660, <i>P</i> < 0.001 in women), RBA (<i>r</i> = 0.328, <i>P</i> < 0.001 to <i>r</i> = 0.371, <i>P</i> < 0.001 in women), AIx (<i>r</i> = 0.659, <i>P</i> < 0.001 to <i>r</i> = 0.738, <i>P</i> < 0.001 in men; <i>r</i> = 0.547, <i>P</i> < 0.001 to <i>r</i> = 0.573, <i>P</i> < 0.001 in women), DAI (<i>r</i> = 0.517, <i>P</i> < 0.001 to <i>r</i> = 0.532, <i>P</i> < 0.001 in men; <i>r</i> = 0.507, <i>P</i> < 0.001 to <i>r</i> = 0.570, <i>P</i> < 0.001 in women) and PTT (<i>r</i> = 0.526, <i>P</i> < 0.001 to <i>r</i> = 0.659, <i>P</i> < 0.001 in men; <i>r</i> = 0.577, <i>P</i> < 0.001 to <i>r</i> = 0.814, <i>P</i> < 0.001 in women).</p><p><strong>Conclusion: </strong>The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.</p>","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023627/pdf/","citationCount":"1","resultStr":"{\"title\":\"Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression.\",\"authors\":\"Qingfeng Tang, Shoujiang Xu, Mengjuan Guo, Guangjun Wang, Zhigeng Pan, Benyue Su\",\"doi\":\"10.1007/s13755-022-00172-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal.</p><p><strong>Methods: </strong>Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices.</p><p><strong>Results: </strong>Compared with the CA, the VA is closer to PW (<i>r</i> = 0.539, <i>P</i> < 0.001 to <i>r</i> = 0.589, <i>P</i> < 0.001 in men; <i>r</i> = 0.325, <i>P</i> < 0.001 to <i>r</i> = 0.400, <i>P</i> < 0.001 in women), IPA (<i>r</i> = - 0.446, <i>P</i> < 0.001 to <i>r</i> = - 0.534, <i>P</i> < 0.001 in men; <i>r</i> = - 0.623, <i>P</i> < 0.001 to <i>r</i> = - 0.660, <i>P</i> < 0.001 in women), RBA (<i>r</i> = 0.328, <i>P</i> < 0.001 to <i>r</i> = 0.371, <i>P</i> < 0.001 in women), AIx (<i>r</i> = 0.659, <i>P</i> < 0.001 to <i>r</i> = 0.738, <i>P</i> < 0.001 in men; <i>r</i> = 0.547, <i>P</i> < 0.001 to <i>r</i> = 0.573, <i>P</i> < 0.001 in women), DAI (<i>r</i> = 0.517, <i>P</i> < 0.001 to <i>r</i> = 0.532, <i>P</i> < 0.001 in men; <i>r</i> = 0.507, <i>P</i> < 0.001 to <i>r</i> = 0.570, <i>P</i> < 0.001 in women) and PTT (<i>r</i> = 0.526, <i>P</i> < 0.001 to <i>r</i> = 0.659, <i>P</i> < 0.001 in men; <i>r</i> = 0.577, <i>P</i> < 0.001 to <i>r</i> = 0.814, <i>P</i> < 0.001 in women).</p><p><strong>Conclusion: </strong>The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.</p>\",\"PeriodicalId\":46312,\"journal\":{\"name\":\"Health Information Science and Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2022-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023627/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health Information Science and Systems\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s13755-022-00172-0\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/12/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Information Science and Systems","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s13755-022-00172-0","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/12/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 1
摘要
目的:血管年龄(Vascular age, VA)是反映血管老化的直接指标，在公共卫生中具有特殊的作用。如何方便、廉价地获取VA一直是研究的热点。本研究提出了一种利用腕部脉搏信号评估VA的新方法。方法:首先采用混合高斯模型(MGM)对脉冲信号进行拟合提取形状特征，并采用主成分分析(PCA)对形状特征进行维数优化;其次，分别以主成分和实足年龄作为自变量和因变量，建立支持向量回归(SVR)模型;第三，将主成分输入到SVR模型中，对受试者血管老化进行预测。最后，将VA与脉宽(PW)、拐点面积比(IPA)、b/a比(RBA)、增强指数(AIx)、舒张增强指数(DAI)、脉冲传递时间(PTT)的相关系数与CA与这6个指标的相关系数进行比较。结果:与CA相比,我们更接近PW (r = 0.539, P r = 0.589, P r = 0.325, P r = 0.400, P r = - 0.446, P r = - 0.534, P r = - 0.623, P r = - 0.660, P r = 0.328, P r = 0.371, P r = 0.659, P r = 0.738, P r = 0.547, P r = 0.573, P r = 0.517, P r = 0.532, P r = 0.507, P r = 0.570, P r = 0.526, P r = 0.659, P r = 0.577, P r = 0.814, P结论:VA比CA更能代表血管老化，为直接、客观地评价公共卫生血管老化提供了一种新的方法。
Wrist pulse signal based vascular age calculation using mixed Gaussian model and support vector regression.
Purpose: Vascular age (VA) is the direct index to reflect vascular aging, so it plays a particular role in public health. How to obtain VA conveniently and cheaply has always been a research hotspot. This study proposes a new method to evaluate VA with wrist pulse signal.
Methods: Firstly, we fit the pulse signal by mixed Gaussian model (MGM) to extract the shape features, and adopt principal component analysis (PCA) to optimize the dimension of the shape features. Secondly, the principal components and chronological age (CA) are respectively taken as the independent variables and dependent variable to establish support vector regression (SVR) model. Thirdly, the principal components are fed into the SVR model to predicted the vascular aging of each subject. The predicted value is regarded as the description of VA. Finally, we compare the correlation coefficients of VA with pulse width (PW), inflection point area ratio (IPA), Ratio b/a (RBA), augmentation index (AIx), diastolic augmentation index (DAI) and pulse transit time (PTT) with those of CA with these six indices.
Results: Compared with the CA, the VA is closer to PW (r = 0.539, P < 0.001 to r = 0.589, P < 0.001 in men; r = 0.325, P < 0.001 to r = 0.400, P < 0.001 in women), IPA (r = - 0.446, P < 0.001 to r = - 0.534, P < 0.001 in men; r = - 0.623, P < 0.001 to r = - 0.660, P < 0.001 in women), RBA (r = 0.328, P < 0.001 to r = 0.371, P < 0.001 in women), AIx (r = 0.659, P < 0.001 to r = 0.738, P < 0.001 in men; r = 0.547, P < 0.001 to r = 0.573, P < 0.001 in women), DAI (r = 0.517, P < 0.001 to r = 0.532, P < 0.001 in men; r = 0.507, P < 0.001 to r = 0.570, P < 0.001 in women) and PTT (r = 0.526, P < 0.001 to r = 0.659, P < 0.001 in men; r = 0.577, P < 0.001 to r = 0.814, P < 0.001 in women).
Conclusion: The VA is more representative of vascular aging than CA. The method presented in this study provides a new way to directly and objectively assess vascular aging in public health.
期刊介绍：
Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.
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