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Updated: Jul 26, 2025
Non-invasive Assessment of Microvascular and Endothelial Function
Published on: January 29, 2013
Uncertainty quantification of microcirculatory characteristic parameters for recognition of cardiovascular diseases
Jianjun Yan1, Shiyu Cai2, Xianglei Cai2
1School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, China; Shanghai Key Laboratory of Intelligent Sensing and Detection Technology, East China University of Science and Technology, Shanghai 200237, China.
Insights
Wearable devices can now monitor cardiovascular health by analyzing pulse waves. Machine learning models accurately identify cardiovascular diseases, achieving over 88% accuracy in distinguishing healthy, hypertensive, and coronary heart disease states.
Area of Science:
- Biomedical Engineering
- Cardiovascular Health Monitoring
- Machine Learning in Healthcare
Background:
- Cardiovascular disease is a leading global cause of death, yet largely preventable.
- Pulse wave analysis offers early insights into cardiovascular function and disease trends.
- Wearable devices are emerging for convenient, long-term mobile health monitoring.
Purpose of the Study:
- To develop wearable devices for acquiring physiological signals for mobile healthcare.
- To quantify microcirculation parameters using a zero-dimensional model and optimization algorithms.
- To construct a feature set for cardiovascular parameters and identify diseases using machine learning.
Main Methods:
- Acquired wrist and fingertip pulse waves from 323 healthy individuals and patients.
- Established a fingertip microcirculation blood flow model and quantified parameters using the slime mold algorithm (SMA).
- Developed a cardiovascular disease identification model using the Random Forest (RF) algorithm on microcirculatory parameters.
Main Results:
- The Random Forest (RF) algorithm demonstrated superior classification performance.
- The model achieved over 88% accuracy in identifying cardiovascular health states.
- Specific accuracies included 95.51% for coronary heart disease, 92.11% for healthy individuals, and 88.55% for hypertensive patients.
Conclusions:
- The developed wearable device supports daily cardiovascular disease monitoring.
- A combined physical and machine learning model successfully quantified microcirculation parameters and identified cardiovascular diseases.
- Machine learning offers a novel approach for cardiovascular health monitoring via pulse wave analysis.
Background:
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.
Objective:
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.
Methods:
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 forest algorithm (RF).
Results:
RF showed good classification performance among the four classification algorithms. The identification accuracy of the model built on the microcirculatory characteristic parameter set and RF algorithm all reached more than 88%. The highest recognition accuracy was 95.51% for coronary heart disease samples, 92.11% for healthy samples, and 88.55% for hypertensive samples. It can be seen that the model based on RF algorithm has a good ability to distinguish the characteristic parameters in different cardiovascular health states.
Conclusions:
The wearable device designed in this paper can facilitate the daily health monitoring of cardiovascular disease. By using the combination of the physical model and machine learning model, the uncertainty quantification of microcirculation parameters and the identification of cardiovascular disease was finally completed. The recognition model based on machine learning provides a new idea and method for the research of cardiovascular health monitoring through pulse waves.
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