Primary study on recognition of vascular stiffness based on wavelet scattering neural network.
10.7507/1001-5515.202207068
- Author:
Shuqi REN
1
;
Zengsheng CHEN
1
;
Xiaoyan DENG
1
;
Yubo FAN
1
;
Anqiang SUN
1
Author Information
1. Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, P. R. China.
- Publication Type:Journal Article
- Keywords:
Cardiovascular diseases;
Korotkoff signal;
Long short-term memory;
Neural network;
Vascular stiffness;
Wavelet scattering
- MeSH:
Humans;
Vascular Stiffness;
Neural Networks, Computer;
Cardiovascular Diseases/diagnosis*;
Sensitivity and Specificity
- From:
Journal of Biomedical Engineering
2023;40(2):244-248
- CountryChina
- Language:Chinese
-
Abstract:
Cardiovascular disease is the leading cause of death worldwide, accounting for 48.0% of all deaths in Europe and 34.3% in the United States. Studies have shown that arterial stiffness takes precedence over vascular structural changes and is therefore considered to be an independent predictor of many cardiovascular diseases. At the same time, the characteristics of Korotkoff signal is related to vascular compliance. The purpose of this study is to explore the feasibility of detecting vascular stiffness based on the characteristics of Korotkoff signal. First, the Korotkoff signals of normal and stiff vessels were collected and preprocessed. Then the scattering features of Korotkoff signal were extracted by wavelet scattering network. Next, the long short-term memory (LSTM) network was established as a classification model to classify the normal and stiff vessels according to the scattering features. Finally, the performance of the classification model was evaluated by some parameters, such as accuracy, sensitivity, and specificity. In this study, 97 cases of Korotkoff signal were collected, including 47 cases from normal vessels and 50 cases from stiff vessels, which were divided into training set and test set according to the ratio of 8 : 2. The accuracy, sensitivity and specificity of the final classification model was 86.4%, 92.3% and 77.8%, respectively. At present, non-invasive screening method for vascular stiffness is very limited. The results of this study show that the characteristics of Korotkoff signal are affected by vascular compliance, and it is feasible to use the characteristics of Korotkoff signal to detect vascular stiffness. This study might be providing a new idea for non-invasive detection of vascular stiffness.
