心电自动分析系统的研究
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摘要
心血管疾病是当今危害人类健康的主要疾病之一,心电图检查是临床上诊断心血管疾病的重要方法。心电图准确的自动分析与诊断对于心血管疾病的诊断起着关键作用。随着计算机技术、数字信号处理技术、人工智能等先进理论的发展,心电计算机自动分析技术的研究也不断向纵深发展。将心电自动分析技术用于动态心电图中,使用计算机对所记录的长达24小时的心电数据进行自动分析处理,可以大大减轻海量数据给医生带来的负担。
本研究总结了前人的成果,提出了一种医师经验与工程分析手段结合的研究模式。由于涉及的内容过多,实际研究采用了一种动态可扩展的框架。在现阶段的研究中,主要以模拟医师临床诊断经验为主,并结合工程上的数据分析手段进行部分病例的判别研究。
目前,本研究主要展开了以下几方面的工作:
①介绍一种以提升小波变换实现心电信号滤波处理的方法。原始心电信号中存在三种主要干扰:肌电干扰、基线漂移和工频干扰。该方法首先采用提升小波变换将原始心电信号分解为不同频段下的逼近信号和细节信号。其次根据心电信号的特征,用阈值滤波方法对细节信号进行处理,最后再用提升小波逆变换重建心电信号,就能实现心电信号中主要干扰的消除。
②研究了基于下采样的心电特征点的标定。该方法首先对滤波后的心电信号进行下采样,使得心电数据量大大减少;其次在下采样的心电信号中利用幅值和一阶导数划分各个心拍及对每个心拍的P波、Q波、R波、S波、T波的识别;最后对下采样得到的各个特征点进行更新,从而完成心电特征点的标定。下采样检测算法基本不受采样频率的影响,幅值和一阶导数采用自适应阈值选择技术,并采用回溯技术对可能遗漏的R波进行再次检测,使得R波的识别能够达到较高的准确率,从而为后续的分析工作奠定了良好的基础。
③研究了心律失常和波形分类的识别算法。该方法通过检测每个心拍中各个特征点之间的时间差和幅度差,以及与临近心拍之间的各个特征点的时间来确定心拍是否正常。对每类心律失常的波形进行相似性划分,使得众多心拍被划分为十几种不同特征的波形。该检测算法可以根据病人心电波形的自身特点来进行差异性参数阈值选择,并且能够达到满意的分类效果。
④研究了嵌入式平台的构建。嵌入式系统可运行于多种硬件平台,可裁剪,性能优异,应用软件丰富,使用成本低,强大的网络功能,GUI(图形用户界而)开发支持和丰富的开发技术资源使得它在医疗软件开发中的地位越来越显著,嵌入式平台采用linux操作系统,linux系统源码开放,并且有个庞大的支持者群体,开发技术文档齐全,对于编写GUI程序提供了良好的开发平台。
⑤编写了嵌入式心电自动分析软件。该软件采用qt开发编写的。软件包括病历库的管理,心电波形的显示,心电信号的滤波,特征点标注、心律失常分析、波形分类等功能。该软件可以处理不同采样频率的心电数据,一至十二通道的心电数据,并可局部放大心电信号供医师查看和编辑。同时软件对上述算法进行了直观的验证。
Cardiovascular disease is one of disease which is thratening human being’s life. Electrocardiogram (ECG) checking is one of the important ways of diagnosing Cardiovascular disease. Exact ECG automatic analyzing and diagnosing plays an important role in diagnosing Cardiovascular disease. Following with the development of advanced theory, computer technique digital signal process technique and artificial intelligence etal, research of ECG computer automatic analyzing technique continues developing in alternate traversing direction. ECG Automatic analyzing is used in Dynamic Electrocardiogram (DCG). The load which large datas cause is rapidly reduced with the help of automatic analyzing 24 hours’ECG data which recorded by computer.
A research model based on others fruit is proposed in this paper and it can integrate experience of ECG specialists and the technologies of engineering. However, the model involved too much work, so a research frame which can be extended dynamic is used in practice. The work preformed at present focused on the utilization of the experience of specialists, and some data analyses method in engineering are also utilized in this work.
The following work has been performed in this research:
①Introduce a way of filtering ECG signals based on lifting scheme wavelet. There are three interferences which appeared frequently, including myoelectric interference, baseline wamder, frequency interference and it’s harmonic component. First, the method destruct original ECG signals into approach signals and delta signals which has different frequency range with lifting scheme wavelet. Second, deal the delta signal with proper threshold by the characteristic of ECG signals. Finally, reconstruct the signals with the reverse of lifting scheme wavelet and erase the main noise of ECG signals.
②It is researched to detect the ECG characteristic by down sampling. First, the method down sampling the ECG signals which has been filtered to reduce the data size of ECG signals. Second, detect P Wave, Q Wave, R Wave, S Wave, T Wave of every throb by amplitude and first difference threshold in down sampling ECG data. Finally, update every characteristic point by down sampling to finish detecting ECG characteristic. down sampling algorithm has no affect on the change of sampling frequency. Amplitude and first difference threshold use self adapting selecting technique. Second detection is processed by back tracking technique in potential omitting R wave. The techniques which mentioned above are able to obtain high percentage of accuracy and make a good foundation to followed analyzing.
③It is researched to detect arrhythmia and wave classify. The method judge the throb normal or not through detecting difference time and difference amplitude in every characteristic of the throb, and the period between two adjacent throb. Classifying similar wave from every arrhythmia wave. Then hundreds of throbs are divided into tens of waves which have different characteristic each other. The method select threshold through patient’s ECG wave characteristic and approach satisfied effect.
④It is researched to build embedded platform. Embedded system has advantages such as running many hardware platform, clipping core, good performance, rich in software resource, low use-cost, strong network function , supporting graphics user interface and rich in development resource. The position is more and more obvious in developing medical software. Embedded platform use linux operator system. linux operator system opens its source code and owns heavy supporting population. The develop document is complete. It provides good develop platform for writing GUI program.
⑤The Embedded ECG automatic analyzing software is developed. The software use qt to develop. It contains case history database manager, ECG wave display, ECG signals filter, ECG characteristic detecting, arrhythmia analyzing, wave classify, case report printing and so on. The software could deal ECG data which has difference sampling frequency or one to twelve channels. It also zoom out local ECG signals to view and edit for doctor. The algorithm which mentioned above is certificated by the software.
本研究总结了前人的成果,提出了一种医师经验与工程分析手段结合的研究模式。由于涉及的内容过多,实际研究采用了一种动态可扩展的框架。在现阶段的研究中,主要以模拟医师临床诊断经验为主,并结合工程上的数据分析手段进行部分病例的判别研究。
目前,本研究主要展开了以下几方面的工作:
①介绍一种以提升小波变换实现心电信号滤波处理的方法。原始心电信号中存在三种主要干扰:肌电干扰、基线漂移和工频干扰。该方法首先采用提升小波变换将原始心电信号分解为不同频段下的逼近信号和细节信号。其次根据心电信号的特征,用阈值滤波方法对细节信号进行处理,最后再用提升小波逆变换重建心电信号,就能实现心电信号中主要干扰的消除。
②研究了基于下采样的心电特征点的标定。该方法首先对滤波后的心电信号进行下采样,使得心电数据量大大减少;其次在下采样的心电信号中利用幅值和一阶导数划分各个心拍及对每个心拍的P波、Q波、R波、S波、T波的识别;最后对下采样得到的各个特征点进行更新,从而完成心电特征点的标定。下采样检测算法基本不受采样频率的影响,幅值和一阶导数采用自适应阈值选择技术,并采用回溯技术对可能遗漏的R波进行再次检测,使得R波的识别能够达到较高的准确率,从而为后续的分析工作奠定了良好的基础。
③研究了心律失常和波形分类的识别算法。该方法通过检测每个心拍中各个特征点之间的时间差和幅度差,以及与临近心拍之间的各个特征点的时间来确定心拍是否正常。对每类心律失常的波形进行相似性划分,使得众多心拍被划分为十几种不同特征的波形。该检测算法可以根据病人心电波形的自身特点来进行差异性参数阈值选择,并且能够达到满意的分类效果。
④研究了嵌入式平台的构建。嵌入式系统可运行于多种硬件平台,可裁剪,性能优异,应用软件丰富,使用成本低,强大的网络功能,GUI(图形用户界而)开发支持和丰富的开发技术资源使得它在医疗软件开发中的地位越来越显著,嵌入式平台采用linux操作系统,linux系统源码开放,并且有个庞大的支持者群体,开发技术文档齐全,对于编写GUI程序提供了良好的开发平台。
⑤编写了嵌入式心电自动分析软件。该软件采用qt开发编写的。软件包括病历库的管理,心电波形的显示,心电信号的滤波,特征点标注、心律失常分析、波形分类等功能。该软件可以处理不同采样频率的心电数据,一至十二通道的心电数据,并可局部放大心电信号供医师查看和编辑。同时软件对上述算法进行了直观的验证。
Cardiovascular disease is one of disease which is thratening human being’s life. Electrocardiogram (ECG) checking is one of the important ways of diagnosing Cardiovascular disease. Exact ECG automatic analyzing and diagnosing plays an important role in diagnosing Cardiovascular disease. Following with the development of advanced theory, computer technique digital signal process technique and artificial intelligence etal, research of ECG computer automatic analyzing technique continues developing in alternate traversing direction. ECG Automatic analyzing is used in Dynamic Electrocardiogram (DCG). The load which large datas cause is rapidly reduced with the help of automatic analyzing 24 hours’ECG data which recorded by computer.
A research model based on others fruit is proposed in this paper and it can integrate experience of ECG specialists and the technologies of engineering. However, the model involved too much work, so a research frame which can be extended dynamic is used in practice. The work preformed at present focused on the utilization of the experience of specialists, and some data analyses method in engineering are also utilized in this work.
The following work has been performed in this research:
①Introduce a way of filtering ECG signals based on lifting scheme wavelet. There are three interferences which appeared frequently, including myoelectric interference, baseline wamder, frequency interference and it’s harmonic component. First, the method destruct original ECG signals into approach signals and delta signals which has different frequency range with lifting scheme wavelet. Second, deal the delta signal with proper threshold by the characteristic of ECG signals. Finally, reconstruct the signals with the reverse of lifting scheme wavelet and erase the main noise of ECG signals.
②It is researched to detect the ECG characteristic by down sampling. First, the method down sampling the ECG signals which has been filtered to reduce the data size of ECG signals. Second, detect P Wave, Q Wave, R Wave, S Wave, T Wave of every throb by amplitude and first difference threshold in down sampling ECG data. Finally, update every characteristic point by down sampling to finish detecting ECG characteristic. down sampling algorithm has no affect on the change of sampling frequency. Amplitude and first difference threshold use self adapting selecting technique. Second detection is processed by back tracking technique in potential omitting R wave. The techniques which mentioned above are able to obtain high percentage of accuracy and make a good foundation to followed analyzing.
③It is researched to detect arrhythmia and wave classify. The method judge the throb normal or not through detecting difference time and difference amplitude in every characteristic of the throb, and the period between two adjacent throb. Classifying similar wave from every arrhythmia wave. Then hundreds of throbs are divided into tens of waves which have different characteristic each other. The method select threshold through patient’s ECG wave characteristic and approach satisfied effect.
④It is researched to build embedded platform. Embedded system has advantages such as running many hardware platform, clipping core, good performance, rich in software resource, low use-cost, strong network function , supporting graphics user interface and rich in development resource. The position is more and more obvious in developing medical software. Embedded platform use linux operator system. linux operator system opens its source code and owns heavy supporting population. The develop document is complete. It provides good develop platform for writing GUI program.
⑤The Embedded ECG automatic analyzing software is developed. The software use qt to develop. It contains case history database manager, ECG wave display, ECG signals filter, ECG characteristic detecting, arrhythmia analyzing, wave classify, case report printing and so on. The software could deal ECG data which has difference sampling frequency or one to twelve channels. It also zoom out local ECG signals to view and edit for doctor. The algorithm which mentioned above is certificated by the software.
引文
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[2] http://wwww.windrug.com (大众医学网)
[3] 杨友春. 远程医疗的现状及发展趋势. 金陵医院学报, 1998,Vol.11,No.4:340-343.
[4] 王建民. 临床医学信息学[M]. 北京, 军事医学科学出版社, 2002
[5] 王学龙. 嵌入式 Linux 系统设计与应用. 北京, 清华大学出版社, 2001
[6] 戚建新,卞正中,杨强. 基于微机的心电信号实时自动分析系统. 北京生物医学工程,1997,16(3):157-161
[7] 刘欣桃. 心电信号的计算机辅助分析方法的研究. 计算机辅助设计与图形学学报,1997,9(3):116-122
[8] 周衍椒,张镜如. 心电图的电生理学基础. 太原,山西人民出版社, 1982.75-95.
[9] 魏太星. 临床心电图学及图谱. 郑州,河南科学技术出版社,1990.
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