Uncertainty quantification of microcirculatory characteristic parameters for recognition of cardiovascular diseases

• Published in: Computer methods and programs in biomedicine 2023-10, Vol.240, p.107674-107674, Article 107674
• Main Authors: Yan, Jianjun, Cai, Shiyu, Cai, Xianglei, Zhu, Guangyao, Zhou, Wei, Guo, Rui, Yan, Haixia, Wang, Yiqin
• Format: Article
• Language: English
• Subjects: Cardiovascular disease recognition; Pulse wave; Random forest algorithm; Slime mold algorithm; Uncertainty quantification
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2023.107674

•Based on the concept of mobile health care, wearable devices are developed to obtain physiological signals.•Combine the zero-dimensional model and the optimization algorithm to complete the uncertainty quantification of the microcirculation parameters.Then, build a better feature set.•The machine learning algorithm can be used to build a model that can accurately realize cardiovascular disease identification. Cardiovascular disease is one of the leading causes of death worldwide. However, according to studies, 90% of cardiovascular diseases can be prevented. Cardiovascular function parameters are an important basis for the diagnosis of cardiovascular diseases. The pulse wave also contains a wealth of physiological and pathological information, which can reflect the trend of cardiac function parameters at an early stage, so the measurement and analysis of the pulse wave signal becomes more and more important. The wearable pulse signal acquisition device has gradually become a new trend. In the mobile health scenario, convenient use is the prerequisite for long-term and rapid health monitoring. The data containing diverse pulse wave signals is the basis for obtaining more comprehensive and accurate human physiopathological information. Accurate data analysis and processing is the key to realizing the important goal of cardiovascular health monitoring. Based on the concept of mobile health care, wearable devices are developed to obtain physiological signals. The zero-dimensional model and the optimization algorithm are combined to complete the uncertainty quantification of the microcirculation parameters. Then, a feature set containing the cardiovasvular parameters can be constructed. The machine learning algorithm can be used to build a model that can accurately realize cardiovascular disease identification. This paper adopts laboratory-developed equipment to acquire the wrist pulse wave and fingertip volume pulse wave. A total of 323 samples were collected from healthy populations, hypertensive patients and patients with coronary heart disease (CHD). The pulse blood flow model in fingertip microcirculation is established, and the uncertainty quantification of model parameters is completed based on slime mold algorithm (SMA). After comparing and analyzing the performance of four algorithms on pulse wave classification, the identification model of cardiovascular diseases is established based on the microcirculatory characteristic parameter set and random for