基于激光自混合效应的心血管医学信号高分辨率传感研究

Cardiovascular disease is a serious damage of high mortality rate to human health, but many existing clinical diagnosis and detection methods have the disadvantages of the electrical connection and body contact. Laser self-mixing effect can be applied to non-contact and non-destructive measurement and diagnosis for medical signals of cardiovascular disease, so it has aroused widespread concern.The application project plan to:. 1)Build a high-resolution sensing system based on laser self-mixing effect, measure the motorial or physical state of artery relevant to cardiovascular with no destruction, and research on obtainment of medical signals, including pulse wave transit time and pulse wave velocity;. 2)Study the self-adaptive algorithm which is suitable for different system parameters, explore environment noise and equipment disturbance’s influence on SNR, realize real-time display of self-mixing signals and accurate reconstruction of medical signals;. 3)Study the correlation between self-mixing signals and medical characteristics, acquire the parameters of self-mixing signals that represent disease information, reveal the relationship between the physiological activity rules and model parameters of cardiovascular system.. The project is expected to solve the major scientific problems and technical difficulties that existed in cardiovascular assessment and diagnosis systems based on laser self-mixing effect, provide a scientific basis for non-destructive measurement and diagnosis of cardiovascular disease and exploit new ways on cardiovascular health assessment and disease diagnosis.

心血管疾病是严重危害人类健康的一大高死亡率疾病，但现有的临床诊断和检测方法都存在电连接和体接触。激光自混合效应可用于心血管医学信号的非接触无损测量和诊断，因此倍受关注。本申请项目计划：. 1）构建基于激光自混合效应的高分辨率传感系统，对人体心血管所涉及的动脉运动或物理状态进行无损测量，研究获取脉搏波传导时间和脉搏波速度等医学信号；. 2）研究适用于不同系统参数的自适应算法，探索环境噪声和设备扰动对信噪比的影响，实现自混合信号的实时显示与医学信号的准确重构；. 3）研究自混合信号与医学特征的相关性，获得具有疾病表征信息的自混合信号参量，揭示心血管系统各项生理活动的规律及各参数间的关系。. 本项目有望解决激光自混合效应应用于心血管评估诊断系统中存在的主要科学问题和技术难点，为心血管疾病的无损测量和诊断提供科学依据，开拓心血管健康评估和疾病诊断的的新途径。

心血管疾病是严重危害人类健康的一大高死亡率疾病，对心血管系统的健康评估和疾病的临床诊断显得至关重要，但现有的临床诊断和检测方法都存在电连接和体接触。激光自混合测量技术作为一种非接触的高精度微振动测量手段，已在血液脉动、血液压力波等医学信号的获取上取得了一定的进展，其克服了传统的测量方法在人体特定部位夹持压力传感器而影响测试结果的准确性，以及部分患者对测量电极片有过敏反应的缺陷。因此，基于激光自混合效应构建用于心血管医学信号的非接触无损测量和诊断系统成为当前研究的一个热点方案。. 本项目提出以新型高分辨微振动测量技术——激光自混合效应为核心，结合嵌入式系统信号分析处理方案，通过模型构建和实验研究，以期有效地获取和重构人体脉搏波，进而得到表征心血管状态的特征参量。着重研究：基于嵌入式的自混合参数估计与信号重构系统，改进电路和优化重构算法降低噪声对信号的影响，研究自混合信号与医学信号之间的相关性；浅表动脉部位自混合信号的高精度获取方法，从时间、波速等对脉博波进行多角度分析，解析出若干个与心血管特征相对应的特征参量。通过该项目研究，实现了采用激光自混合测量技术直接获取到人体浅表微振动的自混合信号，采用光路优化和硬件电路及软件算法实现高分辨率和同步多路传感，结合嵌入式的多线程信号分析处理系统，实现实时自混合信号显示与医学信号重构。项目取得的主要创新性成果：基于自混合干涉的双通道脉搏波传导时间测量，并构建相应的血压模型；基于小波多分辨率自混合信号分析结合极限学习机(ELM)实现连续的血压监测；基于支持向量机(SVM)的脉搏波分类，进而分析预测人体生理状态；基于光程倍增的时-频域分析实现纳米级极弱微振动测量。本项目明确了可通过测量脉搏波引起皮肤表面微颤来重构脉搏波，进而提取脉搏波特征参数以评估心血管医学信号，获得了激光自混合技术应用于心血管医学信号检测和诊断的有效方案和关键技术。

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