本文選題：動(dòng)態(tài)脈搏信號(hào)　切入點(diǎn)：動(dòng)態(tài)脈率變異性　出處：《蘭州理工大學(xué)》2015年博士論文

【摘要】：脈搏信號(hào)和脈率變異性信號(hào)中蘊(yùn)含著豐富的有關(guān)心血管系統(tǒng)的生理和病理信息,可用于心血管疾病的預(yù)防、診斷和治療。脈搏信號(hào)易于采集,已成為便攜式醫(yī)療設(shè)備的常用信號(hào)之一。檢測(cè)動(dòng)態(tài)脈搏信號(hào),從中提取并實(shí)時(shí)分析脈率變異性信號(hào),提取相關(guān)信息,對(duì)心血管疾病的在線監(jiān)護(hù)與預(yù)警具有重要意義。本文介紹了脈搏信號(hào)和脈率變異性信號(hào)的生理機(jī)理和臨床價(jià)值,綜述了脈搏信號(hào)檢測(cè)系統(tǒng)與檢測(cè)方法及脈率變異性提取與分析方法的國(guó)內(nèi)外研究現(xiàn)狀。在已有的研究基礎(chǔ)上,針對(duì)脈搏信號(hào)檢測(cè)過程中噪聲和干擾抑制及信號(hào)干擾段檢測(cè)和質(zhì)量評(píng)估問題,以及現(xiàn)有脈率變異性信號(hào)提取和分析方法準(zhǔn)確性和實(shí)時(shí)性不能兼顧的矛盾,提出了相應(yīng)的解決方法,并將其用于動(dòng)態(tài)脈搏信號(hào)檢測(cè)及脈率變異性信號(hào)實(shí)時(shí)提取與分析。論文主要的研究成果為:1)對(duì)于動(dòng)態(tài)脈搏信號(hào)中一些可抑制的噪聲和干擾,提出了自調(diào)參數(shù)整系數(shù)濾波法,用于不同信噪比的動(dòng)態(tài)脈搏信號(hào)濾波。在整系數(shù)濾波器的基礎(chǔ)上,定義信號(hào)光滑度,用于評(píng)價(jià)濾波結(jié)果及自行調(diào)節(jié)濾波器參數(shù)。將所提出方法用于仿真和實(shí)測(cè)脈搏信號(hào),實(shí)驗(yàn)結(jié)果表明,相比于常用的濾波方法,所提出方法可快速有效地抑制不同信噪比動(dòng)態(tài)脈搏信號(hào)中可抑制噪聲和干擾。2)對(duì)動(dòng)態(tài)脈搏信號(hào)中一些不能通過濾波方法抑制的干擾段,提出干擾段分類檢測(cè)法,并根據(jù)干擾段檢測(cè)結(jié)果進(jìn)行信號(hào)質(zhì)量評(píng)估。根據(jù)干擾段的特點(diǎn)將其分為脈沖干擾、脈搏信號(hào)丟失段和運(yùn)動(dòng)偽跡,提出相應(yīng)的檢測(cè)方法,即干擾段分類檢測(cè)法。通過國(guó)際公認(rèn)數(shù)據(jù)庫(kù)及課題組實(shí)測(cè)脈搏信號(hào)驗(yàn)證,結(jié)果表明,相比于常用的干擾段檢測(cè)方法,所提出的方法可快速準(zhǔn)確地檢測(cè)出脈搏信號(hào)中的干擾段。同時(shí),根據(jù)干擾段檢測(cè)結(jié)果,對(duì)信號(hào)進(jìn)行質(zhì)量評(píng)估。去除質(zhì)量低的信號(hào)段,為進(jìn)一步動(dòng)態(tài)脈搏信號(hào)處理提供信號(hào)質(zhì)量保證。3)根據(jù)脈搏信號(hào)時(shí)域和頻域特征,總結(jié)常用脈率變異性信號(hào)提取方法的優(yōu)缺點(diǎn),提出了自適應(yīng)幅度閾值法、基于改進(jìn)滑窗迭代DFT的脈率變異性提取法和基于Hilbert-Huang變換的脈率變異性提取法。將所提出的方法用于仿真和實(shí)測(cè)脈搏信號(hào),實(shí)驗(yàn)結(jié)果表明,相比于其它方法,滑窗迭代DFT(基波)法可準(zhǔn)確實(shí)時(shí)地提取脈率變異性信號(hào),且對(duì)脈搏信號(hào)的信噪比和采樣頻率變化不敏感,可用于動(dòng)態(tài)脈率變異性信號(hào)實(shí)時(shí)提取。4)對(duì)于脈率變異性信號(hào)的一些時(shí)域和非線性分析方法存在的實(shí)時(shí)性不高、算法運(yùn)算量大等不足,采用滑窗迭代思想對(duì)其改進(jìn),并就改進(jìn)后方法是否用于心血管疾病識(shí)別進(jìn)行了探索。對(duì)時(shí)域法、龐加萊散點(diǎn)圖法、基本尺度熵分析法和符號(hào)序列熵分析法進(jìn)行改進(jìn),并將其用于實(shí)測(cè)脈率變異性信號(hào)分析。結(jié)果表明,相比之下,改進(jìn)后方法可在脈率變異性信號(hào)數(shù)據(jù)點(diǎn)更新的同時(shí),快速對(duì)其進(jìn)行分析,提取相應(yīng)的參數(shù),實(shí)時(shí)性得到質(zhì)的提高,可用于動(dòng)態(tài)脈率變異性信號(hào)實(shí)時(shí)分析。將改進(jìn)方法用于分析國(guó)際公認(rèn)數(shù)據(jù)中的年輕人和老年人、健康人和冠心病人的脈率變異性信號(hào),提取一些參數(shù)組成特征向量,用于智能學(xué)習(xí)方法分類。相比之下,分類的準(zhǔn)確率很高,表明所改進(jìn)的方法可用于一些心血管疾病的識(shí)別。5)基于智能手機(jī)平臺(tái),研制了動(dòng)態(tài)脈搏信號(hào)檢測(cè)與處理系統(tǒng),驗(yàn)證所提出方法實(shí)用性。通過實(shí)際檢測(cè)和處理動(dòng)態(tài)脈搏信號(hào),結(jié)果表明:所提出的自調(diào)參數(shù)整系數(shù)濾波法、基于干擾段分類檢測(cè)的信號(hào)質(zhì)量評(píng)估法可用于動(dòng)態(tài)脈搏信號(hào)檢測(cè);所提出的基于改進(jìn)滑窗迭代DFT(基波)法,所改進(jìn)的脈率變異性信號(hào)分析法可準(zhǔn)確實(shí)時(shí)地提取并分析動(dòng)態(tài)脈率變異性信號(hào),提取相關(guān)的參數(shù)。為進(jìn)一步實(shí)現(xiàn)心血管疾病在線監(jiān)護(hù)與預(yù)警提供保障。
[Abstract]:The pulse signal and the pulse rate variability signal contained in the physiological and pathological information about the cardiovascular system of the rich, can be used for cardiovascular disease prevention, diagnosis and treatment. The pulse signal is easy to be collected, has become one of the most commonly used signal portable medical equipment. The detection of dynamic pulse signal, extracted from real-time analysis and pulse rate variability signal and extract relevant information, plays an important role in online monitoring and early warning of cardiovascular diseases. This paper introduces the clinical value and physiological mechanism of pulse signal and pulse rate variability signals, the research status of pulse signal detection system and detection method and the method of extraction and analysis of pulse rate variability was reviewed based on the existing research. On the pulse signal detection in noise and interference suppression and signal interference detection and quality evaluation, and the current pulse rate variability signal extraction and Analysis of the contradiction between accuracy and real-time method can not take into account, puts forward corresponding solving methods, and applied to real time extraction and analysis of variability of dynamic signal pulse signal detection and pulse rate. The main research results are as follows: 1) for some can suppress noise and interference of pulse signal, the self adjusting parameter integer coefficient filter method for dynamic pulse signal filtering different SNR. Based on integer coefficient filter, defined signal smoothness, and adjust the filtering results for the evaluation of the filter parameters. The proposed method is used for the simulation and the measured pulse signal, the experimental results show that compared with common filter methods, the proposed the method can effectively suppress the different SNR of dynamic pulse signal can suppress noise and interference.2) of interference section cannot be suppressed by dynamic pulse signal filtering method is proposed. Interference segment classification detection method, and signal quality assessment based on the interference section of test results. According to the characteristics of interference section will be divided into pulse interference, pulse signal loss and motion artifacts, the corresponding detection methods, namely interference segment classification method. Through the internationally recognized data base and research group measured pulse signal verification. The results show that, compared to the interference detection method used, the proposed method can detect the interference section of the pulse signal quickly and accurately. At the same time, according to the interference period of test results, to evaluate the quality of the signal. The signal segment removal of low quality, provide quality assurance for the further dynamic.3 signal processing of pulse signal according to the pulse) signal time domain and frequency domain feature extraction method, summarize the advantages and disadvantages of common pulse rate variability signal, the adaptive amplitude threshold method, the improved sliding window iterative DFT extraction based on pulse rate variability And the method based on Hilbert-Huang transform pulse rate variability extraction method. The proposed method is applied to the simulation and measurement of pulse signal, the experimental results show that compared with other methods, sliding window iterative DFT (Ji Bo) accurate real-time extraction of pulse rate variability signals, and the signal-to-noise ratio and the sampling frequency is not sensitive to the change of the pulse signal, can be used for dynamic pulse rate variability signal extraction.4) for real-time presence of pulse rate variability signals time domain and the nonlinear analysis method is not high, lack of a large amount of computation, using a sliding window iterative thought to its improvement, and the improved method is used to identify the cardiovascular disease exploration on. Time domain method, Poincare plot method, basic scale entropy analysis and sequence entropy analysis method was improved and used for the analysis of measured pulse rate variability signals. The results show that, compared with the improved Method can be used in pulse rate variability signal data update at the same time, the rapid analysis of the extraction of the corresponding parameters, real-time quality improvement, can be used for dynamic pulse rate variability signal analysis. The improved method is used to analyze the data of the internationally recognized young people and older people, healthy people Wacom heart patients the pulse rate variability signal, extracting some parameters of feature vector for intelligent learning method classification. By contrast, the classification accuracy is very high, show that the improved method can be used for the identification of some cardiovascular diseases).5 intelligent mobile phone platform is developed based on dynamic pulse signal detection and processing system, verify the applicability of the proposed methods. Through the actual detection and processing of dynamic pulse signal, the results show that the self adjusting parameter coefficient filtering method is proposed, based on the signal quality assessment method of interference detection can be used for dynamic segment classification Pulse signal detection; improved sliding window iterative algorithm of DFT based on the proposed (Ji Bo) method, the improved pulse rate variability signal analysis can be accurately extracted in real time and dynamic analysis of pulse rate variability signals, extract the relevant parameters. For the further implementation of online monitoring and early warning of cardiovascular disease to provide protection.

【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN911.23;R540.4
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本文編號(hào)：1695179