摘要
血管硬化是心血管疾病的独立预测因子,柯氏音的特征与血管顺应性密切相关。本研究的目的是探究基于柯氏音信号的特征进行血管硬化检测的可行性。分别采集正常血管和硬化血管的柯氏音信号,并进行预处理,利用小波散射网络对柯氏音信号进行散射特征提取,搭建长短期记忆网络(LSTM)作为分类模型,对散射特征进行分类,评估LSTM分类模型的性能。本研究共有97例柯氏音信号数据,其中血管硬化组为50例,血管正常组为47例,按照8∶2的比例划分为训练集和测试集。最终分类模型的准确率为86.4%,敏感度为92.3%,特异性为77.8%。研究结果表明,柯氏音信号的特征受到血管顺应性的影响,利用柯氏音信号的特征进行血管硬化的检测是可行的,本研究为无创血管硬化检测提供了一种新的思路。
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.
作者
任淑琪
陈增胜
邓小燕
樊瑜波
孙安强
REN Shuqi;CHEN Zengsheng;DENG Xiaoyan;FANYubo;SUN Anqiang(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)
出处
《生物医学工程学杂志》
EI
CAS
北大核心
2023年第2期244-248,共5页
Journal of Biomedical Engineering
基金
国家自然科学基金(12172033,11872096,32071311,11862004)。
关键词
柯氏音
血管硬化
心血管疾病
小波散射
长短期记忆网络
神经网络
Korotkoff signal
Vascular stiffness
Cardiovascular diseases
Wavelet scattering
Long short-term memory
Neural network
