基于桡动脉波形分析的动脉硬化评估指标研究
摘要
心脑血管疾病是严重威胁人类健康的重大疾病。动脉血管的硬化程度反应了人体潜在的心血管疾病的可能。由于桡动脉脉搏信号中包涵着人体重要的生理和病理信息,并且容易获取,因此对桡动脉脉搏波波形分析,就能够从中提取出一些反映血管状况的生理参数,可以获知动脉血管的硬化状况,能够早期的预测和评估动脉血管硬化程度。
因此,本研究通过对不同年龄段心血管健康人群的桡动脉脉搏波波形进行检测和分析,从中找出了能够反映人体动脉血管硬化状况的指标。本论文主要内容包括以下几个方面：
1)首先介绍了桡动脉脉搏信号的获取平台,主要概述了系统的总体硬件架构和软件功能模块。硬件部分采用了基于PVDF的传感器,实现了脉搏信号的便携式连续采集的要求；软件模块主要实现信号的预处理、检波和分析等功能。
2)详细介绍了脉搏波信号采集过程中存在的几种噪声,根据各种噪声和脉搏波自身的特点,利用数学形态学的方法消除了采集的脉搏信号中的基线漂移,并利用小波变换将含肌电噪声的脉搏信号进行了多尺度分解,在重构过程中实现了消噪,同时描述了信号的检波。
3)对桡动脉脉搏波进行了时域特征、频域特征和小波域特征的分析和提取。并采用小波变换技术对脉搏信号的难以识别的年轻人和老年人波形的重搏波前波位置进行了定位。
4)对获得的脉搏波相应各域的生理信息进行了特征选择和识别分类,对不同年龄人群脉搏波波形特点进行了总结,选取了时域特征、频域特征和小波域特征中的一些特征参数。
5)根据融合的各域选取的特征进行分类识别,从脉搏波信息中选出最能表征血管自然硬化的指标参数,提出了从时域分析中得到的较为新颖的用于评估动脉硬化的参数指标。
Cardiovascular disease is the primary cause of mortality and a frequent contributor to morbidity throughout the world. Previous studies have shown that arterial stiffness is an inevitable process that occurs during aging and strongly associated with CV risk factors. Therefore, testing arterial stiffness with non-invasive techniques offers an opportunity to evaluate cardiovascular risk assessment for asymptomatic people in research and clinical laboratories.
Analysis on wrist pulse signal is a low cost non-invasive method of obtaining physiological and pathological information of people which is portable and could be widely used in the family to monitor the physiological status. It can be characterized by characteristic parameters of the arteries situation and can be detected for predicting the early change of vascular conditions.
The purpose of the paper included as follows:
First, the paper introduced the radial artery pulse signal acquisition platform, outlined the overall system hardware architecture and software modules. By using PVDF-based sensors, the pulse signal can be achieved continuously.
Second, according to different noise characteristics which existed in pulse wave signal acquisition process, we use mathematical morphology approach to eliminate the baseline drift of the pulse signal and use wavelet transform to eliminate noise.
Third, we analyze the radial artery pulse wave based on time-domain and frequency domain and wavelet domain characteristics. Wavelet transform is used for signal feature extraction.
Fourth, corresponding to the feature selection of physiological information we obtained a higher classification, and the characteristics of pulse waveforms at different age groups were summarized.
Last, a novel parameter from time domain analysis is proposed to assess atherosclerosis.
因此,本研究通过对不同年龄段心血管健康人群的桡动脉脉搏波波形进行检测和分析,从中找出了能够反映人体动脉血管硬化状况的指标。本论文主要内容包括以下几个方面：
1)首先介绍了桡动脉脉搏信号的获取平台,主要概述了系统的总体硬件架构和软件功能模块。硬件部分采用了基于PVDF的传感器,实现了脉搏信号的便携式连续采集的要求；软件模块主要实现信号的预处理、检波和分析等功能。
2)详细介绍了脉搏波信号采集过程中存在的几种噪声,根据各种噪声和脉搏波自身的特点,利用数学形态学的方法消除了采集的脉搏信号中的基线漂移,并利用小波变换将含肌电噪声的脉搏信号进行了多尺度分解,在重构过程中实现了消噪,同时描述了信号的检波。
3)对桡动脉脉搏波进行了时域特征、频域特征和小波域特征的分析和提取。并采用小波变换技术对脉搏信号的难以识别的年轻人和老年人波形的重搏波前波位置进行了定位。
4)对获得的脉搏波相应各域的生理信息进行了特征选择和识别分类,对不同年龄人群脉搏波波形特点进行了总结,选取了时域特征、频域特征和小波域特征中的一些特征参数。
5)根据融合的各域选取的特征进行分类识别,从脉搏波信息中选出最能表征血管自然硬化的指标参数,提出了从时域分析中得到的较为新颖的用于评估动脉硬化的参数指标。
Cardiovascular disease is the primary cause of mortality and a frequent contributor to morbidity throughout the world. Previous studies have shown that arterial stiffness is an inevitable process that occurs during aging and strongly associated with CV risk factors. Therefore, testing arterial stiffness with non-invasive techniques offers an opportunity to evaluate cardiovascular risk assessment for asymptomatic people in research and clinical laboratories.
Analysis on wrist pulse signal is a low cost non-invasive method of obtaining physiological and pathological information of people which is portable and could be widely used in the family to monitor the physiological status. It can be characterized by characteristic parameters of the arteries situation and can be detected for predicting the early change of vascular conditions.
The purpose of the paper included as follows:
First, the paper introduced the radial artery pulse signal acquisition platform, outlined the overall system hardware architecture and software modules. By using PVDF-based sensors, the pulse signal can be achieved continuously.
Second, according to different noise characteristics which existed in pulse wave signal acquisition process, we use mathematical morphology approach to eliminate the baseline drift of the pulse signal and use wavelet transform to eliminate noise.
Third, we analyze the radial artery pulse wave based on time-domain and frequency domain and wavelet domain characteristics. Wavelet transform is used for signal feature extraction.
Fourth, corresponding to the feature selection of physiological information we obtained a higher classification, and the characteristics of pulse waveforms at different age groups were summarized.
Last, a novel parameter from time domain analysis is proposed to assess atherosclerosis.
引文
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