基于多源信息融合的心血管监护系统研究
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摘要
心音信号是人体最重要的生理信号之一,富含心脏各个部分如心房、心室及心血管功能状态的大量病理信息,是临床评估心脏功能状态的最基本参数。在心血管疾病尚未发展到足以产生临床及病理改变之前,心音中出现的杂音和畸变就是重要的信息。脉搏信号同样携带有丰富的心血管健康状况信息,因此,记录心音和脉搏图对于心血管监护具有重要的参考价值。
本文在分析国内外有关研究现状的基础上,对多源信息融合理论作了较为详细的介绍,分析几种融合方法的优缺点,并最终选用了在处理不确定信息方面具有明显优势的Dempster-Shafer证据理论。考虑到监护系统一般要求具有良好的实时处理能力,而监护系统的数据量又非常大,且要以最快的速度来到达检测目标,此时D-S证据理论就显得有些力不从心,为了弥补理论的不足而影响系统的实时性,我们借助了具有良好的非线性映射能力和自学习能力的BP神经网络。
在监护系统软件设计方面,以图形化虚拟仪器开发软件LabVIEW 8.2为平台,开发出了友好的监护系统界面,主要有实时采集界面、信号分析界面和记录管理界面;系统软件设计主要实现心音、脉搏信号的采集、显示、信号处理、特征提取、存储、回放等功能。
在监护系统的数据存储方面,本文采用数据库技术,研究开发了病员管理数据库。考虑到监护系统对数据库实时访问的要求,本文选用LabSQL实现对数据库的访问,数据库选用广泛使用的小型桌面关系型数据库管理系统——Microsoft Access 2003。
本次研究综合运用了多源信息融合技术、神经网络技术、数字信号处理技术、数据库技术,等等。本文设计的监护系统通过数字化心音、脉搏波等传感器采集与心血管健康相关的心音、脉搏波等信号,经过计算机的处理与分析获取心音、脉搏等特征参数,利用智能化的信息融合方法观察人体的心血管功能是否正常。整个系统本身具有为无创伤、实时性好等优点。
Heart sound signal is one of the most primary physiological signals.It contains a great deal of pathological information which belongs to each parts of heart,such as atria,ventricle and cardiovascular functional status.It is also an important essential parameter used to evaluate heart functional status. Before the development of cardiovascular disease having not yet been sufficient to produce clinical and pathological changes, the appearance of noise and distortion in phonocardiogram indicates some important information.There is abundant cardiovascular health information in pulse signal,so it has significant reference value for cardiovascular monitoring to record phonocardiogram and pulse diagram.
At the basis of analyzing the situation of related research at home and aroad,this thesis has made more detailed description of multi-source information fusion theory. What's more,it has analyzed the advantages and disadvantages of several fusion methods. Dempster-Shafer evidence theory has been selected fmally,which has obvious advantages in dealing with uncertain information.Considering the weakness of D-S evidence theory and the requirement of monitoring system for real-time processing capability, reaching target dection as soon as possible,we resort to the BP neural network which has a good nonlinear mapping ability and self-learning ability.
On the software design of monitoring system side,three friendly panels of monitoring system have been completed,including real-time sampling panel,signal analysis panel and record management panel at the platform of graphic virtual instrument development software LabVIEW 8.2. The software design mainly realizes heart sound and pulse sampling,display,processing,characteristics extracting,storage, playback,etc.
On the data storage of monitoring system side,this thesis designs patients management database by use of database technology.In view of monitoring system visiting database real-timely, LabSQL is chosen to access database developed by Microsoft Access 2003,which is a small desktop relational database management system.
This study makes good use of multi-source information fusion technology, neural network technology,digital signal processing technology,database technology,ect.The designed monitoring system acquires heart sound signal and pulse signal from digital heart sound sensor and pulse sensor,which reflect cardiovascular health.The characteristic parameters are obtained by virtue of computer processing and analysis. The use of intelligent information fusion method is used to observe whether the body's cardiovascular function is normal or not. The entire system itself has non-invasive, real-time advantages and so on.
本文在分析国内外有关研究现状的基础上,对多源信息融合理论作了较为详细的介绍,分析几种融合方法的优缺点,并最终选用了在处理不确定信息方面具有明显优势的Dempster-Shafer证据理论。考虑到监护系统一般要求具有良好的实时处理能力,而监护系统的数据量又非常大,且要以最快的速度来到达检测目标,此时D-S证据理论就显得有些力不从心,为了弥补理论的不足而影响系统的实时性,我们借助了具有良好的非线性映射能力和自学习能力的BP神经网络。
在监护系统软件设计方面,以图形化虚拟仪器开发软件LabVIEW 8.2为平台,开发出了友好的监护系统界面,主要有实时采集界面、信号分析界面和记录管理界面;系统软件设计主要实现心音、脉搏信号的采集、显示、信号处理、特征提取、存储、回放等功能。
在监护系统的数据存储方面,本文采用数据库技术,研究开发了病员管理数据库。考虑到监护系统对数据库实时访问的要求,本文选用LabSQL实现对数据库的访问,数据库选用广泛使用的小型桌面关系型数据库管理系统——Microsoft Access 2003。
本次研究综合运用了多源信息融合技术、神经网络技术、数字信号处理技术、数据库技术,等等。本文设计的监护系统通过数字化心音、脉搏波等传感器采集与心血管健康相关的心音、脉搏波等信号,经过计算机的处理与分析获取心音、脉搏等特征参数,利用智能化的信息融合方法观察人体的心血管功能是否正常。整个系统本身具有为无创伤、实时性好等优点。
Heart sound signal is one of the most primary physiological signals.It contains a great deal of pathological information which belongs to each parts of heart,such as atria,ventricle and cardiovascular functional status.It is also an important essential parameter used to evaluate heart functional status. Before the development of cardiovascular disease having not yet been sufficient to produce clinical and pathological changes, the appearance of noise and distortion in phonocardiogram indicates some important information.There is abundant cardiovascular health information in pulse signal,so it has significant reference value for cardiovascular monitoring to record phonocardiogram and pulse diagram.
At the basis of analyzing the situation of related research at home and aroad,this thesis has made more detailed description of multi-source information fusion theory. What's more,it has analyzed the advantages and disadvantages of several fusion methods. Dempster-Shafer evidence theory has been selected fmally,which has obvious advantages in dealing with uncertain information.Considering the weakness of D-S evidence theory and the requirement of monitoring system for real-time processing capability, reaching target dection as soon as possible,we resort to the BP neural network which has a good nonlinear mapping ability and self-learning ability.
On the software design of monitoring system side,three friendly panels of monitoring system have been completed,including real-time sampling panel,signal analysis panel and record management panel at the platform of graphic virtual instrument development software LabVIEW 8.2. The software design mainly realizes heart sound and pulse sampling,display,processing,characteristics extracting,storage, playback,etc.
On the data storage of monitoring system side,this thesis designs patients management database by use of database technology.In view of monitoring system visiting database real-timely, LabSQL is chosen to access database developed by Microsoft Access 2003,which is a small desktop relational database management system.
This study makes good use of multi-source information fusion technology, neural network technology,digital signal processing technology,database technology,ect.The designed monitoring system acquires heart sound signal and pulse signal from digital heart sound sensor and pulse sensor,which reflect cardiovascular health.The characteristic parameters are obtained by virtue of computer processing and analysis. The use of intelligent information fusion method is used to observe whether the body's cardiovascular function is normal or not. The entire system itself has non-invasive, real-time advantages and so on.
引文
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[29]曹先彬,融合神经网络的一种改进遗传算法,模式识别与人工智能[J],1999,12,486-491
[30]张彦释,姜兴渭,多传感器信息融合及其在智能故障诊断中的应用综述,传感器技术[J],1999,18,18-22
[31]李冲祥,神经网络和证据理论集成的数据融合故障诊断方法研究[D],燕山大学,2003
[32]方向林,郭兴明,彭承琳等,运动和呼吸对心率及心音幅值影响的探讨,2000.6
[33]Takashi Matsuyama,Belief Formation from Observation and Belief Integration Using Virtual Belief Space in Dempster-Shafer Probability Model,in Proceedings of the IEEE International Conference,Las Vegas,NV.1994,10,379-385
[34]Wang Ping,Yang Genqing,Improvement method for thecombining role of Dempster-Shafer evidence theory based on reliability,Journal of Systems Engineering and Electronics,2005,2,471-474
[35]张佑春,王平,张公永,基于D-S证据理论和神经网络的信息融合方法及应用,第27届中国控制会议论文集,2008,5,623-626
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[40]韦哲,李战明,程自峰等,基于LabVIEW 8.2的心音信号检测与分析系统的研究,医疗卫生装备,2008.7,(29)7:8-9
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[2]韩崇昭,朱洪艳,段战胜等著,多源信息融合,北京:清华大学出版社,2006
[3]吴延军,徐径平,心音的产生与传导机制,生物医学工程学杂志,1996,13(3):280-288
[4]张晓丹,赵海,王刚等,基于信息融合的故障诊断模糊专家系统的应用,吉林大学学报(工学版)[J],2004,34(1):141-145
[5]S Lukkarinen A-L Noponen K Sikio A Angerla,A New Phonocardiographic Recording System.Helsinki University of Technology,Espoo,Finland,IEEE,1997
[6]A Haghighi-Mood,J N Torry,APPLICATION OF ADVANCED SIGHAL PROCESSING TECHNIQUED IN ANALYSIS OF HEART SOUND,The Institution of Eletrical Engineers,1995
[7]周玉芳,马虹,何蕾等,计算机辅助的心血管信号检测和处理系统的研究,中山医科大学学报,2000,21:附8-附11
[8]程敬之,王海滨,王衍文等,心音信号的识别与分类,生物医学工程学杂志,1999,16,382-387
[9]于云之,聂邦畿,心音的临床意义及研究现状,现代医学仪器与应用,1997,9,9-12
[10]肖仪华,裴驭力等,基于笔记本计算机的心音分析仪,北京生物医学工程,1999.3
[11]王文辉,陈端荣,常蕴等,便携式心音分析仪的研制,中国医疗器械杂志,1994.3
[12]章秀淦,微型心音听诊器薄膜混合集成电路掩模研制,微细加工技术,1995.9
[13]李树钧,纪震,牟轩沁等,利用Internet和RAS实现远程医疗,中国医疗器械杂志,1999,23:19-24
[14]牛广玉,侯维宁,多通道信号的同相位整周期采样,数据采集与处理,1997,12,114-116
[15]王炳和,脉搏系统建模与脉象信息分析的研究进展,生物医学工程学杂志,2002:19(2):329-333
[16]Zaiton Sharif Mohd Shamian Zainal Ahmad Zuri Sha'ameri Sheikh Hussain Shaikh Salleh,ANALYSIS AND CALSSIFICATIONOF HEART SOUNDS AND MURMURS BASED ON THE INSTANTANEOUS ENERGY AND FREQUENCY ESTIMATIONS,Digital Sighal Procedding Lab Dept of Microelectronics and Computer Engineering Faulty of Electricaol Engineering University Teknologi Malaysia 81310NTM-Skudai,IEEE,2000
[17]B.H.Tan M.Moghavvemi,Real Time Analysis of Fetal Phonocardiography.Dept.of Electrical engineering,Faculty of engineering,University of Malaya,50603 Kuala Lumpur,Malaysia.IEEE,2000
[18]L B Dahl,P Hasvold,E Arid,et al,Heart murmurs recorded by a sensor based electronic stethoscope and e-mailed for remote assessment.Arch Dis Child,2002,87,297-301
[19]程正兴,小波分析算法与应用,西安:西安交通大学出版社,1998年5月第1版
[20]杨福生,小波变换的工程分析与应用,北京:科学出版社,1999年2月第1版
[21]Qiuzhen Xue,Yu Hen Hu,W.J.Tompkins-Neutral-Network-Based Adaptive Matched Filtering for QRS Detection.IEEE Trans.Biomed.Eng.,vol.39,pp 317-329,APR 1992
[22]姬军,董秀珍,心电信号QRS波的识别算法及程序设计,北京生物医学工程,2001,20(2):119
[23]史忠植,神经网络计算[M],北京:电子工业出版社,1993,189-192
[24]胡守仁,神经网络应用技术[M],长沙:国防科技大学出版社,1998,152-157
[25]虞和济,陈长征,张省等,基于神经网络的智能诊断[M],冶金工业出版社,北京:2000,105-109
[26]闻新,周露,王丹力等,MATLAB神经网络应用设计[M],科学出版社,北京:2001,110-113
[27]阎平凡,张长水,人工神经网络与模拟进化计算[M],清华大学出版社,北京:2000,231-235
[28]袁曾任,人工神经元网络及其应用[M],清华大学出版社,北京:2000,134-135
[29]曹先彬,融合神经网络的一种改进遗传算法,模式识别与人工智能[J],1999,12,486-491
[30]张彦释,姜兴渭,多传感器信息融合及其在智能故障诊断中的应用综述,传感器技术[J],1999,18,18-22
[31]李冲祥,神经网络和证据理论集成的数据融合故障诊断方法研究[D],燕山大学,2003
[32]方向林,郭兴明,彭承琳等,运动和呼吸对心率及心音幅值影响的探讨,2000.6
[33]Takashi Matsuyama,Belief Formation from Observation and Belief Integration Using Virtual Belief Space in Dempster-Shafer Probability Model,in Proceedings of the IEEE International Conference,Las Vegas,NV.1994,10,379-385
[34]Wang Ping,Yang Genqing,Improvement method for thecombining role of Dempster-Shafer evidence theory based on reliability,Journal of Systems Engineering and Electronics,2005,2,471-474
[35]张佑春,王平,张公永,基于D-S证据理论和神经网络的信息融合方法及应用,第27届中国控制会议论文集,2008,5,623-626
[36]http://www.physionet.org/physiobank/database/#ecg
[37]宋喜国,邓亲恺,MIT-BIH心率失常数据库的识读及应用,中国医学物理学杂志,2004,21,230-232
[38]LabVIEW User Manual,USA:National Instruments Corporation,1998
[39]朱志强,LabVIEW及其在生物医学工程中的应用[J],国外医学:生物医学工程分册,2001,24(2):59-64
[40]韦哲,李战明,程自峰等,基于LabVIEW 8.2的心音信号检测与分析系统的研究,医疗卫生装备,2008.7,(29)7:8-9
[41]汤家华,王道德,LabVIEW在USB实时数据采集处理系统中的应用,电子器件:第29卷,第2期
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