基于脈搏波傳導(dǎo)時間變異性的冠心病識別方法研究
發(fā)布時間：2018-10-18 18:16
【摘要】：冠心病以其發(fā)病率高、治愈率低等特點成為威脅人類身體健康的最大隱患之一,如果不能有效地預(yù)防治療,冠心病將成為人類未來發(fā)展面臨的嚴(yán)峻問題。隨著智能醫(yī)療儀器的快速發(fā)展,給冠心病的診治帶來了便利,但是冠心病識別還存在準(zhǔn)確率低、檢測耗時較長且費(fèi)用昂貴等缺陷。因此,研究一種實時性好、準(zhǔn)確率高的冠心病識別方法顯得尤為重要。心電信號和脈搏信號蘊(yùn)含豐富的人體生理系統(tǒng)生理和病理信息,可以作為冠心病預(yù)防和識別的指標(biāo)。結(jié)合心電脈搏信號檢測出脈搏波傳導(dǎo)時間變異性信號,脈搏波傳導(dǎo)時間變異性信號能夠反映冠脈病變的嚴(yán)重程度,以及自主神經(jīng)系統(tǒng)的調(diào)節(jié)機(jī)制。實時分析脈搏波傳導(dǎo)時間變異性得到相關(guān)信息,對冠心病的實時監(jiān)護(hù)及預(yù)警具有重要意義。從冠心病的病理機(jī)理和臨床診斷出發(fā),綜述了冠心病臨床診斷方法和冠心病識別方法的國內(nèi)外研究現(xiàn)狀。在綜述方法的基礎(chǔ)上,利用冠心病發(fā)病過程中冠脈病變嚴(yán)重程度和自主神經(jīng)調(diào)控原理,提出采用脈搏波傳導(dǎo)時間變異性來實現(xiàn)冠心病的實時、準(zhǔn)確的識別。解決了現(xiàn)有脈搏波傳導(dǎo)時間變異性信號分析方法實時性和準(zhǔn)確性顧此失彼的問題,以及現(xiàn)有只通過自主神經(jīng)系統(tǒng)調(diào)控原理識別冠心病包涵信息單一的問題。本文主要工作如下:1)研究脈搏波傳導(dǎo)時間變異性信號的提取方法,通過數(shù)據(jù)特征和現(xiàn)有方法的分析對比,確定同步心電信號R波峰值至脈搏信號主波峰值間的時間間隔序列,即為脈搏波傳導(dǎo)時間變異性。針對心電脈搏信號在采集過程中引入的各種噪聲和干擾,采用實時性較強(qiáng)的整系數(shù)濾波器進(jìn)行濾波。2)針對目前脈搏波傳導(dǎo)時間變異性分析方法的主觀性強(qiáng)、實時性差等問題,結(jié)合脈搏波傳導(dǎo)時間變異性的特點,在時域分析和非線性分析的基礎(chǔ)上,采用滑窗迭代的思想對其改進(jìn)。得到實時時域特征和實時非線性特征,通過實驗分析得到改進(jìn)后的特征具有較好的實時性,并且大部分特征具有較好的準(zhǔn)確性。同時分析了脈搏波傳導(dǎo)時間變異性的頻譜信息,較心率變異性頻譜能量分布更明顯。3)根據(jù)脈搏波傳導(dǎo)時間變異性信號的特點確定了各識別算法的模型參數(shù),并通過識別準(zhǔn)確率和算法運(yùn)行時間進(jìn)一步說明參數(shù)的重要性。采用t檢驗和主成分分析進(jìn)行特征選擇,有效保留原始特征信息的同時消減了數(shù)據(jù)維數(shù),從而降低了識別算法的復(fù)雜度。通過實驗對比分析,提出了一種兼顧準(zhǔn)確性和實時性的冠心病識別方法。
[Abstract]:Coronary heart disease (CHD) has become one of the biggest hidden dangers to human health because of its high incidence and low cure rate. If it cannot be effectively prevented and treated, coronary heart disease will become a severe problem in the future development of human beings. With the rapid development of intelligent medical instruments, it brings convenience to the diagnosis and treatment of coronary heart disease, but the recognition of coronary heart disease still has the defects of low accuracy, long time consuming and expensive detection. Therefore, it is very important to study a recognition method of coronary heart disease with good real-time and high accuracy. ECG and pulse signals contain abundant physiological and pathological information of human physiological system and can be used as indicators of prevention and recognition of coronary heart disease. Pulse wave time variability signal can be detected by ECG pulse signal. Pulse wave time variability signal can reflect the severity of coronary artery disease and the regulation mechanism of autonomic nervous system. Real-time analysis of pulse wave time variability is of great significance to real-time monitoring and early warning of coronary heart disease. Based on the pathological mechanism and clinical diagnosis of coronary heart disease (CHD), the research status of clinical diagnosis and recognition of CHD at home and abroad is reviewed. On the basis of the review methods, using the severity of coronary artery disease and the regulation principle of autonomic nerve during the course of coronary heart disease, the pulse wave conduction time variability is proposed to realize the real-time and accurate recognition of coronary heart disease. The problem of real-time and accuracy of pulse wave time variability signal analysis method is solved, and the problem of single information recognition of coronary heart disease by the principle of autonomic nervous system regulation is also solved. The main work of this paper is as follows: 1) the extraction method of pulse wave conduction time variability signal is studied. Through the analysis and comparison of data characteristics and existing methods, the time series between the peak R wave of synchronous ECG signal and the peak value of main wave of pulse signal is determined. That is, pulse wave conduction time variability. Aiming at all kinds of noise and interference introduced in ECG pulse signal acquisition process, the filter with high real time integral coefficient is used to filter it. 2) aiming at the problems of strong subjectivity and poor real-time performance of current pulse wave conduction time variability analysis method, etc. According to the characteristics of pulse wave conduction time variability, based on the time domain analysis and nonlinear analysis, the sliding window iteration is used to improve it. The real-time domain feature and the real time nonlinear feature are obtained. The improved feature has good real-time performance and most of the features have good accuracy. At the same time, the spectrum information of pulse wave conduction time variability is analyzed, which is more obvious than heart rate variability spectrum energy distribution. 3) according to the characteristics of pulse wave conduction time variability signal, the model parameters of each identification algorithm are determined. The importance of the parameters is further explained by the recognition accuracy and the running time of the algorithm. T test and principal component analysis (PCA) are used for feature selection, which can effectively preserve the original feature information and reduce the dimension of the data, thus reducing the complexity of the recognition algorithm. Based on the comparative analysis of experiments, a recognition method of coronary heart disease (CHD) with both accuracy and real time is proposed.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R541.4;TN911.7
本文編號：2279973
[Abstract]:Coronary heart disease (CHD) has become one of the biggest hidden dangers to human health because of its high incidence and low cure rate. If it cannot be effectively prevented and treated, coronary heart disease will become a severe problem in the future development of human beings. With the rapid development of intelligent medical instruments, it brings convenience to the diagnosis and treatment of coronary heart disease, but the recognition of coronary heart disease still has the defects of low accuracy, long time consuming and expensive detection. Therefore, it is very important to study a recognition method of coronary heart disease with good real-time and high accuracy. ECG and pulse signals contain abundant physiological and pathological information of human physiological system and can be used as indicators of prevention and recognition of coronary heart disease. Pulse wave time variability signal can be detected by ECG pulse signal. Pulse wave time variability signal can reflect the severity of coronary artery disease and the regulation mechanism of autonomic nervous system. Real-time analysis of pulse wave time variability is of great significance to real-time monitoring and early warning of coronary heart disease. Based on the pathological mechanism and clinical diagnosis of coronary heart disease (CHD), the research status of clinical diagnosis and recognition of CHD at home and abroad is reviewed. On the basis of the review methods, using the severity of coronary artery disease and the regulation principle of autonomic nerve during the course of coronary heart disease, the pulse wave conduction time variability is proposed to realize the real-time and accurate recognition of coronary heart disease. The problem of real-time and accuracy of pulse wave time variability signal analysis method is solved, and the problem of single information recognition of coronary heart disease by the principle of autonomic nervous system regulation is also solved. The main work of this paper is as follows: 1) the extraction method of pulse wave conduction time variability signal is studied. Through the analysis and comparison of data characteristics and existing methods, the time series between the peak R wave of synchronous ECG signal and the peak value of main wave of pulse signal is determined. That is, pulse wave conduction time variability. Aiming at all kinds of noise and interference introduced in ECG pulse signal acquisition process, the filter with high real time integral coefficient is used to filter it. 2) aiming at the problems of strong subjectivity and poor real-time performance of current pulse wave conduction time variability analysis method, etc. According to the characteristics of pulse wave conduction time variability, based on the time domain analysis and nonlinear analysis, the sliding window iteration is used to improve it. The real-time domain feature and the real time nonlinear feature are obtained. The improved feature has good real-time performance and most of the features have good accuracy. At the same time, the spectrum information of pulse wave conduction time variability is analyzed, which is more obvious than heart rate variability spectrum energy distribution. 3) according to the characteristics of pulse wave conduction time variability signal, the model parameters of each identification algorithm are determined. The importance of the parameters is further explained by the recognition accuracy and the running time of the algorithm. T test and principal component analysis (PCA) are used for feature selection, which can effectively preserve the original feature information and reduce the dimension of the data, thus reducing the complexity of the recognition algorithm. Based on the comparative analysis of experiments, a recognition method of coronary heart disease (CHD) with both accuracy and real time is proposed.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R541.4;TN911.7
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 徐禮勝;周樹然;姚陽;齊林;;脈率變異性估計心率變異性的可行性分析[J];東北大學(xué)學(xué)報(自然科學(xué)版);2017年01期
2 辛毅;郭超;凌振寶;田紅英;李想;戴強(qiáng);;基于壓電薄膜傳感器的穿戴式健康監(jiān)測體域網(wǎng)系統(tǒng)[J];國防科技大學(xué)學(xué)報;2016年06期
3 張柏林;楊承志;吳宏超;;基于AR模型的Yule-Walker法和Burg法功率譜估計性能分析[J];計算機(jī)與數(shù)字工程;2016年05期
4 侯允天;;冠心病能否“早期識別”[J];心腦血管病防治;2016年02期
5 孫新建;曾亞平;蘇振妍;;AR模型在爆破震動信號頻譜分析中的應(yīng)用[J];爆破;2016年01期
6 丁榮晶;;穩(wěn)定性冠心病心臟康復(fù)藥物處方管理專家共識[J];中華心血管病雜志;2016年01期
7 普國全;;冠心病患者心率變異性分析[J];心血管病防治知識(學(xué)術(shù)版);2015年03期
8 柴曉珂;王步青;張政波;王國靜;王衛(wèi)東;;漸進(jìn)性引導(dǎo)呼吸下的脈搏波傳導(dǎo)時間變異性分析[J];生物醫(yī)學(xué)工程學(xué)雜志;2014年06期
9 王明友;張忠濤;;CT冠狀動脈成像與冠狀動脈造影診斷冠心病對照研究[J];中國醫(yī)學(xué)影像學(xué)雜志;2014年11期
10 趙瑩瑩;黃樸忠;李焱;王曉洋;徐升;王新賢;辛浩;;經(jīng)胸冠狀動脈超聲與動態(tài)心電圖對冠心病的診斷初探[J];中國超聲醫(yī)學(xué)雜志;2014年08期
相關(guān)碩士學(xué)位論文 前2條
1 孫磊;基于脈搏波傳導(dǎo)時間變異性的低血壓預(yù)測研究[D];浙江大學(xué);2014年
2 朱悅;SVM在冠心病分類預(yù)測中的應(yīng)用研究[D];華南理工大學(xué);2013年
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