基于脉搏波分析的麻醉手术中无创生理参数监测系统

发布时间：2018-04-08 13:43

本文选题：光电容积脉搏波　切入点：低血压　出处：《浙江大学》2012年博士论文

【摘要】：麻醉手术中的低血压及伤害感受的监测及预测是非常重要的,它可以帮助麻醉医生及时了解病人生理状态的变化。目前市场上已有的麻醉监测设备大多只能提供简单的测量参数显示和报警功能,这些设备中的参数虽多,但却没有建立起这些参数与生理状态之间的联系,也就难以发挥麻醉医生们的主观能动性。随着近几十年来对光电容积脉搏波(PPG)所做的大量临床研究,PPG已被广泛的用于麻醉生理参数监测,该方法安全无创、可靠性高,所以本论文根据PPG研究的生理学基础,设计了基于PPG的麻醉手术中无创生理参数监测系统。本论文的主要研究工作如下： 1.设计了基于光电容积脉搏波的麻醉手术中无创生理参数监测系统,可以完成麻醉过程相关的心率(HR)、血氧饱和度(SpO2)、收缩压、舒张压、平均压、脉搏波传导时间(PTT)、衰减时间常数(RC)、光电容积脉搏波幅度(PPGA)、下降支面积比(ARD)等参数的同步实时监测。其中,RC和ARD均为本系统所独有的生理参数。 2.设计实现了以nRF24L01模块为核心的低功耗无线传输方案。该方案以nRF系列芯片内嵌的Enhanced ShockBurst协议为基础,设计开发了短距离无线传输网络协议(NWSN),实现了数据传输的低功耗。该方案与载波监听多路访问协议(CSMA)相比,极大地降低了数据收发的电流,总功耗约为后者的1/3。同时该方案利用了无线天然无漏电流、无需隔离的特点,节省了硬件成本,降低了设计复杂度。 3.提出了麻醉监测过程中脉搏波传导时间变异性(PTTV)的分析算法,并首次将其应用于麻醉手术中无创生理参数监测系统。本论文提出了一个新的光电容积脉搏波(PPG)信号特征点检测算法。此算法主要采用窗口化的局部查找方法,再与一阶导数和逻辑判断相结合来检测特征点。当接受区间为24.0ms时,该算法的检出率高于95%。由心电图(ECG)信号的R波和PPG信号的P波位置可以计算得到PTT,进而将其作为PTTV的原始输入,采用自回归(AR)模型建立PTTV分析算法。 4.首次将PTTV分析方法应用于麻醉低血压研究。在浙江大学医学院附属妇产科医院，，经过24例临床实验,结果表明,甚低频与低频功率的比值(VLF/LF)以及低频功率与总功率的比值(LF/ALL)这两个参数的P值分别为：PVLf/LF=0.028、PLF/ALL=0.031,说明这两个参数在低血压组与正常组中有明显差异，具有预测麻醉低血压发生的可行性。该系统被应用于麻醉低血压及伤害感受的临床研究,初步实现了对低血压及伤害感受的有效评估。本论文所设计之系统严格按照国家相关标准要求开发,具有很好的临床应用前景。
[Abstract]:Monitoring and prediction of hypotension and injury perception in anaesthesia surgery is very important, which can help anesthesiologist to keep abreast of the changes of patient's physiological state.At present, most of the existing anesthetic monitoring equipment in the market can only provide simple display and alarm functions of measuring parameters. Although there are many parameters in these equipment, there is no relationship between these parameters and physiological state.It is difficult to give full play to the subjective initiative of the anesthesiologist.As a large number of clinical studies have been carried out in recent decades, PPG has been widely used in the monitoring of physiological parameters of anesthesia. This method is safe and non-invasive, and has high reliability. Therefore, this paper based on the physiological basis of PPG research.A non-invasive physiological parameter monitoring system based on PPG was designed.The main research work of this thesis is as follows:1.A non-invasive physiological parameter monitoring system based on photovolumic pulse wave was designed. The system can complete the following functions: HRT, SPO _ 2, systolic blood pressure, diastolic blood pressure, mean pressure, etc.The parameters of pulse wave conduction time (PTT), attenuation time constant (RC), photovolumic pulse wave amplitude (PPGAA) and descending branch area ratio (ARD) are monitored simultaneously and in real time.Among them, RC and ARD are the unique physiological parameters of the system.2.A low power wireless transmission scheme based on nRF24L01 module is designed and implemented.Based on the Enhanced ShockBurst protocol embedded in nRF chips, the short range wireless transmission network protocol (NWSNN) is designed and developed, and the low power consumption of data transmission is realized.Compared with CSMA, the proposed scheme greatly reduces the current of data receiving and receiving, and the total power consumption is about one third of that of the latter.At the same time, the scheme makes use of the characteristics of wireless natural leakage current, no isolation, saves hardware cost and reduces design complexity.3.The analysis algorithm of pulse wave conduction time variability (PTTVV) during anesthesia monitoring was proposed and applied to the monitoring system of noninvasive physiological parameters in anesthesia surgery for the first time.In this paper, a new feature point detection algorithm for optoelectronic volumetric pulse wave (PPG) signal is proposed.This algorithm mainly uses the windowed local search method, and then combines with the first order derivative and logical judgment to detect the feature points.The detection rate of the algorithm is higher than that of 95 when the interval is accepted as 24.0ms.The position of R wave and P wave of PPG signal can be calculated from the position of R wave and P wave of electrocardiogram (ECG) signal, which can be taken as the original input of PTTV, and the PTTV analysis algorithm is established by using autoregressive PTTV model.4.For the first time, PTTV analysis was applied to the study of anaesthesia hypotension.In the Department of Obstetrics and Gynecology, affiliated to Zhejiang University Medical College, 24 cases of clinical trials have been conducted. The results show thatThe ratio of very low frequency to low frequency (VLF / LFL) and the ratio of low frequency power to total power (LFR / ALL) were 0. 028% PLF / ALL 0.031, respectively, which indicated that these two parameters had obvious difference between hypotension group and normal group, and it was feasible to predict the occurrence of anaesthesia hypotension.The system has been applied to the clinical study of anaesthesia hypotension and injury perception.The system designed in this paper is developed in strict accordance with relevant national standards and has a good prospect of clinical application.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2012
【分类号】：R318.6

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