本文選題：光電容積脈搏波描記法 + 生理指數(shù)��； 參考：《東北大學》2011年碩士論文

【摘要】：光電容積脈搏波描記法(PPG)是借助于光電技術手段,在活體組織中檢測血液容積變化的一種無創(chuàng)檢測方法。由于血液容積在人體心臟的周期性脈動作用下會發(fā)生周期性的脈動變化,所以通過光接收器的光強也隨之呈脈動性的變化,將獲得的光信號轉化為電信號即是光電容積脈搏波。容積脈搏波中包含很多有關于心血管系統(tǒng)的重要信息,但是目前為止,利用光電手段得到的人體生理指數(shù)只有脈率和血氧飽和度,在其他生理指數(shù)的測量中或者僅僅停留在理論階段,或者不能對其進行標定。在實際測量人體PPG信號時,由于儀器中電子元件所產(chǎn)生的噪聲、電磁場的干擾、環(huán)境光的變化以及人體的呼吸和運動等因素的影響,得到的響應常常包含很多的噪聲和基線漂移。 針對上述問題,首先設計了基于小波變換的低通濾波器和基于經(jīng)驗模態(tài)分解(EMD)的去除基線漂移算法,對容積脈搏波進行濾波去噪處理,去除包括工頻在內的高頻干擾和低頻基線漂移；然后提出了基于微分閾值法、梯形面積法和指數(shù)加權移動平均(EWMA)的特征參數(shù)值提取算法,提取PPG波形的特征參數(shù),并利用這些特征參數(shù)計算出人體脈率、脈率變異性、動脈血壓、血氧飽和度、心搏出量、心輸出量、外周阻力、動脈順應性、心搏指數(shù)、心臟指數(shù)等人體生理指數(shù),同時本文還提出了基于滑動窗口的奇異點閾值剔除算法,結合人體生理機能將超出生理范圍內的奇異點剔除；最后對動脈血壓和波形特征參數(shù)進行相關性和回歸分析,分別構造出收縮壓和舒張壓與波形特征參數(shù)的回歸方程。 實驗表明,利用本文所提出的算法可以有效的提取光電容積脈搏波的波形特征參數(shù),計算出的人體生理指數(shù)與其它儀器檢測的結果基本吻合,可以應用到無創(chuàng)光電監(jiān)測儀中。此外,利用回歸分析構造回歸方程計算人體動脈血壓為血壓的無創(chuàng)測量提供了種新的手段和方法。
[Abstract]:PPG is a noninvasive method for detecting the changes of blood volume in living tissues by means of photoelectric technique. Because the blood volume changes periodically under the action of the periodic pulsation of the human heart, the intensity of the light through the light receiver also fluctuates with it. The obtained light signal is converted into an electrical signal, that is, the photovolumic pulse wave. Volume pulse waves contain a lot of important information about the cardiovascular system, but so far, only pulse rates and oxygen saturation are the only physiological indices obtained by optoelectronic means. Other physiological indices are measured only at the theoretical level or can not be calibrated. In the actual measurement of human PPG signal, due to the noise produced by electronic components in the instrument, the interference of electromagnetic field, the change of environmental light and the breathing and movement of human body, etc. The resulting response often contains a lot of noise and baseline drift. To solve the above problems, a low-pass filter based on wavelet transform and a baseline drift removal algorithm based on empirical mode decomposition (EMD) are designed, and the volumetric pulse wave is filtered and de-noised. After removing high frequency interference and low frequency baseline drift including power frequency, a method based on differential threshold method, trapezoidal area method and exponentially weighted moving average (EWMA) is proposed to extract the characteristic parameters of PPG waveform. And using these characteristic parameters to calculate human body physiological indexes such as pulse rate, pulse rate variability, arterial blood pressure, blood oxygen saturation, cardiac output, peripheral resistance, arterial compliance, cardiac beat index, cardiac index, etc. At the same time, this paper also proposed a sliding window based singular point threshold elimination algorithm, combined with human physiological functions to remove the singular points beyond the physiological range. Finally, the correlation and regression analysis of arterial blood pressure and waveform characteristic parameters are carried out. The regression equations of systolic and diastolic blood pressure and waveform characteristic parameters are constructed respectively. The experimental results show that the proposed algorithm can effectively extract the waveform characteristic parameters of photovolumic pulse wave, and the calculated body physiological index is in good agreement with the results of other instruments, and can be applied to the non-invasive photoelectric monitor. In addition, using regression analysis to construct regression equation to calculate human arterial blood pressure provides a new method for noninvasive measurement of blood pressure.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：R331;TP274

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