本文選題：指端脈搏波 + 血壓測量��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：基于脈搏波測量血壓的方法是現(xiàn)階段無創(chuàng)連續(xù)血壓測量的重要研究分支�；诿}搏波的血壓測量方法包括基于脈搏波傳導(dǎo)速度的測量方法、基于脈搏波傳導(dǎo)時間的測量方法和基于脈搏波特征參數(shù)的測量方法。前兩種測量方法需要采集兩路人體生理信號,而兩路信號間的穩(wěn)定性對測量結(jié)果有較大的影響。后一種通過分析脈搏波特征參數(shù)與血壓之間的關(guān)系,建立脈搏波特征參數(shù)-血壓計算模型,實(shí)現(xiàn)對每搏血壓的連續(xù)測量�，F(xiàn)階段基于脈搏波特征參數(shù)的血壓測量方法主要集中于對肱動脈和橈動脈脈搏波的研究,血壓計算模型多是通過回歸分析方法建立的線性回歸方程,模型一般適用于特定的測量個體和脈搏波采集設(shè)備。指端脈搏波與胲動脈、橈動脈脈搏波具有較好的相似性,且更容易被測量,已被用于各類心血管生理參數(shù)的計算。移動智能終端軟硬件性能的快速提升,使得基于指端脈搏波進(jìn)行血壓測量的便捷性得到了極大提高,具有良好的應(yīng)用前景。本文研究了指端脈搏波特征參數(shù)與血壓之間的關(guān)系,提出了一種基于指端脈搏波的血壓測量方法,并在移動終端上進(jìn)行了初步實(shí)踐。首先,基于對指端脈搏波和現(xiàn)有的脈搏波特征參數(shù)的分析,提出了本文采用的指端脈搏波特征參數(shù),并通過分析各參數(shù)與血壓之間的相關(guān)性,為利用這些參數(shù)計算血壓提供了理論依據(jù)。然后,本文給出了針對兩種脈搏波的獲取、預(yù)處理和特征參數(shù)提取方法。一種脈搏波取自MIMIC數(shù)據(jù)庫,用于血壓計算模型的建立和評估;另一種脈搏波利用智能手機(jī)采集,用于檢驗血壓計算模型在實(shí)際場景下的性能。其次,本文給出了血壓計算模型的建立和評估方法。本文采用偏最小二乘回歸分析和BP神經(jīng)網(wǎng)絡(luò)兩種方法建立脈搏波特征參數(shù)-血壓計算模型,前者能在自變量間存在較高相關(guān)性的情況下擬合自變量和因變量間的關(guān)系;后者則能夠很好地擬合自變量和因變量間的非線性關(guān)系。評估結(jié)果顯示,利用BP神經(jīng)網(wǎng)絡(luò)建立的模型具有更好的血壓預(yù)測能力。最后,基于當(dāng)前移動端慢病管理的背景,將本文建立的模型在移動端進(jìn)行了初步實(shí)踐——開發(fā)了一個基于Android的血壓測量模塊,并在已有的移動端慢病管理平臺進(jìn)行了初步應(yīng)用。
[Abstract]:Pulse wave based blood pressure measurement is an important branch of noninvasive continuous blood pressure measurement. Pulse wave based blood pressure measurement method includes pulse wave velocity measurement method, pulse wave conduction time measurement method and pulse wave characteristic parameter measurement method. The first two methods need to collect two human physiological signals, and the stability between the two signals has a great influence on the measurement results. By analyzing the relationship between pulse wave characteristic parameters and blood pressure, a pulse wave characteristic parameter-blood pressure calculation model is established to realize the continuous measurement of stroke blood pressure. At present, blood pressure measurement methods based on characteristic parameters of pulse wave mainly focus on the study of brachial artery and radial artery pulse wave. The calculation models of blood pressure are mostly linear regression equations established by regression analysis. The model is generally suitable for specific measuring individuals and pulse wave acquisition equipment. The fingertip pulse wave has good similarity with hydroxylamine artery and radial artery pulse wave and is more easily measured. It has been used to calculate various cardiovascular physiological parameters. With the rapid improvement of hardware and software performance of mobile intelligent terminal, the convenience of blood pressure measurement based on fingertip pulse wave is greatly improved, and it has a good application prospect. In this paper, the relationship between the characteristic parameters of fingertip pulse wave and blood pressure is studied, and a blood pressure measurement method based on finger pulse wave is proposed, and the preliminary practice is carried out on mobile terminal. Firstly, based on the analysis of the characteristic parameters of finger pulse wave and existing pulse wave, the characteristic parameters of finger pulse wave are put forward, and the correlation between these parameters and blood pressure is analyzed. It provides a theoretical basis for the calculation of blood pressure using these parameters. Then, this paper presents two pulse wave acquisition, preprocessing and feature extraction methods. One pulse wave is taken from the miMIC database for the establishment and evaluation of the blood pressure calculation model, and the other pulse wave is collected by smart phone to test the performance of the blood pressure calculation model in the actual scenario. Secondly, the establishment and evaluation method of blood pressure calculation model are given. In this paper, two methods of partial least square regression analysis and BP neural network are used to establish a pulse wave characteristic parameter-blood pressure calculation model. The former can fit the relationship between independent variables and dependent variables when there is a high correlation between independent variables. The latter can fit the nonlinear relationship between independent variables and dependent variables well. The evaluation results show that the BP neural network model has better blood pressure prediction ability. Finally, based on the background of the current mobile side slow disease management, the model is applied in the mobile side. A blood pressure measurement module based on Android is developed and applied to the existing mobile side chronic disease management platform.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP183;R443.5

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