心电信号分析与虚拟式心电自动分析仪的开发
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摘要
心电图(electrocardiogram,简称ECG)是心脏电活动在体表的综合反映,对心电信号进行分析进而诊断心脏疾病具有重要研究价值。目前对于心电信号分析方法尽管较多,但对于心电特征波的定位分析还不理想,在理论上还有进一步创新的余地。作为实现临床心电检测的心电自动分析仪,目前大多是国外产品,它们价格高昂,市场普及率不高。国内缺少具有完全自主知识产权的产品。虚拟仪器具有的优越性能使其成为测试仪器未来发展的主流方向,因此开发具有完全自主知识产权的虚拟式心电自动分析仪,并将其推向市场,是一项十分有意义的工作。基于以上理论研究的需要和现实市场的需求,作者开展了心电信号处理、分析及心电自动分析仪开发等方面的研究工作。
本文首先综述了当前的心电信号预处理、心电特征分析以及心电自动分析仪器开发的研究现状。
然后在分析几种常用于心电分析仪的快速预处理算法之后,分别研究并设计了基于窗函数的FIR 带通数字滤波器、改进的自适应相干模板去噪算法和小波与LMS自适应算法相结合的去噪算法。它们分别适用于不同的心电信号分析要求。基于窗函数的FIR 带通数字滤波器具有快速实时、简洁高效的特点,能够同时滤去基线漂移和工频干扰,适合于心电监护类仪器,实现长时间采集时的及时分析。自适应相干模板法能够有效滤去工频干扰,但同时也会导致低端截止频率不够低,影响心电低频信息的完整性。本文对此进行了改进,提出了二级级联滤波器的滤波方法:即利用50Hz 模板信号和100Hz 模板信号频谱间的关系,构造出新的只滤除50Hz 的滤波器,然后将其与原自适应相关模板法滤除工频干扰的滤波器级联,组成两级滤波器,解决了低端截止频率和工频干扰滤除效果之间的矛盾。对心电信号多尺度小波变换后,直接把高尺度下的逼近信息置零再重构,可以滤除基线漂移,但它容易导致心电的P、T 波等低频成分失真,本文创新性地将小波变换与LMS自适应算法相结合,根据心电信号多级分解后的特点,将其高尺度下的逼近信息作为LMS 算法的输入端,参考端以常数输入,经LMS 递推运算后,再对ECG 进行重构。实验对比显示:该算法能够在滤去基线漂移的同时,有效降低心电信号中的低频成分(如P 波和T 波)的失真度,为更好的分析P 波和T 波特征信息提取提供了基础。
接着本文重点研究了心电特征信息提取方法。在对两种目前较成熟运用于心电分析仪上的特征波识别算法进行分析、改进和实验之后,作者对小波变换检测信号奇异点原理及当前基于小波的心电特征识别算法进行了研究,在此基础上创新
Electrocardiogram (ECG) is a synthetic reflection of the heart electricity on body surface. It is very valuable to analyze the ECG signal to diagnose the heart disease. At present there are many analysis methods on ECG signal, yet accurately locating the characteristic waves of ECG is still difficult. So there is room for innovation in theory. As the tools of detecting ECG information, automatic ECG analyzers mainly comes from oversea presently, and the products with complete independent intellectual property are very lacking inland, which conduces its higher price and lower market occupancy. Virtual instrument with predominant performances represents the future mainstream direction of measurement instrument. Developing the virtually automatic ECG analyzer with complete independent intellectual property and pushing them to market are obviously worthy. Based on the demands of academic research and market, author has done research work on signal process, ECG analysis methods and developing the automatic ECG analyzer.
In this thesis the currently preprocess and analysis methods of ECG signal and the present status of developing automatic ECG analyzer are first summarized.
Then some fast preprocess algorithms usually used by ECG analyzer are analyzed, and their performances are evaluated. The FIR digital filter based on window function, improved adaptive coherent template algorithm, and wavelet transform and LMS adaptive united algorithm are separately researched and designed. They are applicable for different ECG analysis. The FIR digital filter based on window function is faster, real-time and efficient. Baseline drift and power line interference can be filtered by the filter at the same time. It is applicable for ECG monitor to collect and analysis ECG signal immediately for long time. Power line interference can be cancelled by the adaptive coherent template algorithm, yet it can lead the low critical frequency not lower enough and affect the integrality of low frequency information. The algorithm is improved in this thesis through the second cascade filter. According to the frequency relations between 50Hz template signal and 100Hz template signal, a new filter which only cancels 50Hz is structured. And then it is cascaded with the filter used in previous adaptive coherent template algorithm to cancel power line interference. The method can solve the conflict between critical frequency and power line interference. If the ECG signal is transformed by wavelet and the approach information of higher scale is set
zero, then the base line drift will be cancelled in the reconstructed ECG signal. But the method will result in the distorted low frequency component like P and T waves. In this thesis an innovative algorithm combined wavelet transform and LMS is presented. The approach information at higher scale is used as the input of LMS algorithm and constant as reference value, the new signal is obtained through the LMS operation, which is used as the new approach information to restructure the ECG signal. Massive experiments show that the method can cancel the base line drift and remarkably reduce the degree of distortion of low frequency component, which provide a better foundation to analyze P and T waves. Then the methods of extracting ECG characteristic information are principally researched. Two algorithms identifying ECG characteristic waves which maturely applied in ECG analyzer are analyzed and improved, and validating experiments are done. An in-depth research on the principle of wavelet transform detecting singular point and some algorithms identifying ECG characteristic waves based on wavelet are also researched and evaluated. Then author innovatively presents the united algorithm based on wavelet to identify and locate the ECG characteristic waves. The principle is: the shapes of transformed ECG characteristic waves are classified first, and then the shapes of detected characteristic waves and peaks are determined by the number of modulus maximum pairs produced by transformed ECG signal at specified scale. Finally the onset and offset of characteristic wave will be determined by amplitude and slope criterion. The differentia with common wavelet algorithm is that the united algorithm first determines wave shape and second locates onset and offset, and then different methods are applied in response to different shapes. The precision to locate ECG characteristic waves, especially to P and T waves, is greatly improved by the algorithm. For a long time it is difficult to identify and locate the low amplitude and multi-shape ECG characteristic waves like P and T wave, Using the algorithm can solve the problem. Massive comparison experiments have been down, which shows that the algorithm is exact and reliable. Arrhythmias and heart rate variability (HRV) have been researched deeply in this thesis. Some familiar arrhythmias have been sum up, and seven criterions have been put forward. HRV is researched from time and frequency domain separately. Power spectrum density of HRV signal is analyzed from time-frequency domain using three-dimensional “color spectrum”. The innovative method can directly reflect the energy value of HRV signal at some time and some frequency, which provides for
doctors and researchers more information to analyze ECG signal. The developing technique of virtual instrument (VI) has been research. Component technique is introduced to develop VI, and the developing method of VI based on component technique is presented, which can effectively improve the speed and quality of VI development. Based on above research achievements, author develops the virtually automatic ECG analyzer. The instrument has not only all functions of hardware-based ECG analyzer, but also has richer function, smarter measurement and lower price. Experiments with clinic datum show that compared with results from manual detection the results is validate and steady , and error is within clinic permit. So it can fulfill the clinic requirement. Data-base management function is also developed in the ECG analyzer, which can provide more information and datum for researching and diagnosing cardiovascular disease. Lastly the next research works are presented at the end of the thesis.
本文首先综述了当前的心电信号预处理、心电特征分析以及心电自动分析仪器开发的研究现状。
然后在分析几种常用于心电分析仪的快速预处理算法之后,分别研究并设计了基于窗函数的FIR 带通数字滤波器、改进的自适应相干模板去噪算法和小波与LMS自适应算法相结合的去噪算法。它们分别适用于不同的心电信号分析要求。基于窗函数的FIR 带通数字滤波器具有快速实时、简洁高效的特点,能够同时滤去基线漂移和工频干扰,适合于心电监护类仪器,实现长时间采集时的及时分析。自适应相干模板法能够有效滤去工频干扰,但同时也会导致低端截止频率不够低,影响心电低频信息的完整性。本文对此进行了改进,提出了二级级联滤波器的滤波方法:即利用50Hz 模板信号和100Hz 模板信号频谱间的关系,构造出新的只滤除50Hz 的滤波器,然后将其与原自适应相关模板法滤除工频干扰的滤波器级联,组成两级滤波器,解决了低端截止频率和工频干扰滤除效果之间的矛盾。对心电信号多尺度小波变换后,直接把高尺度下的逼近信息置零再重构,可以滤除基线漂移,但它容易导致心电的P、T 波等低频成分失真,本文创新性地将小波变换与LMS自适应算法相结合,根据心电信号多级分解后的特点,将其高尺度下的逼近信息作为LMS 算法的输入端,参考端以常数输入,经LMS 递推运算后,再对ECG 进行重构。实验对比显示:该算法能够在滤去基线漂移的同时,有效降低心电信号中的低频成分(如P 波和T 波)的失真度,为更好的分析P 波和T 波特征信息提取提供了基础。
接着本文重点研究了心电特征信息提取方法。在对两种目前较成熟运用于心电分析仪上的特征波识别算法进行分析、改进和实验之后,作者对小波变换检测信号奇异点原理及当前基于小波的心电特征识别算法进行了研究,在此基础上创新
Electrocardiogram (ECG) is a synthetic reflection of the heart electricity on body surface. It is very valuable to analyze the ECG signal to diagnose the heart disease. At present there are many analysis methods on ECG signal, yet accurately locating the characteristic waves of ECG is still difficult. So there is room for innovation in theory. As the tools of detecting ECG information, automatic ECG analyzers mainly comes from oversea presently, and the products with complete independent intellectual property are very lacking inland, which conduces its higher price and lower market occupancy. Virtual instrument with predominant performances represents the future mainstream direction of measurement instrument. Developing the virtually automatic ECG analyzer with complete independent intellectual property and pushing them to market are obviously worthy. Based on the demands of academic research and market, author has done research work on signal process, ECG analysis methods and developing the automatic ECG analyzer.
In this thesis the currently preprocess and analysis methods of ECG signal and the present status of developing automatic ECG analyzer are first summarized.
Then some fast preprocess algorithms usually used by ECG analyzer are analyzed, and their performances are evaluated. The FIR digital filter based on window function, improved adaptive coherent template algorithm, and wavelet transform and LMS adaptive united algorithm are separately researched and designed. They are applicable for different ECG analysis. The FIR digital filter based on window function is faster, real-time and efficient. Baseline drift and power line interference can be filtered by the filter at the same time. It is applicable for ECG monitor to collect and analysis ECG signal immediately for long time. Power line interference can be cancelled by the adaptive coherent template algorithm, yet it can lead the low critical frequency not lower enough and affect the integrality of low frequency information. The algorithm is improved in this thesis through the second cascade filter. According to the frequency relations between 50Hz template signal and 100Hz template signal, a new filter which only cancels 50Hz is structured. And then it is cascaded with the filter used in previous adaptive coherent template algorithm to cancel power line interference. The method can solve the conflict between critical frequency and power line interference. If the ECG signal is transformed by wavelet and the approach information of higher scale is set
zero, then the base line drift will be cancelled in the reconstructed ECG signal. But the method will result in the distorted low frequency component like P and T waves. In this thesis an innovative algorithm combined wavelet transform and LMS is presented. The approach information at higher scale is used as the input of LMS algorithm and constant as reference value, the new signal is obtained through the LMS operation, which is used as the new approach information to restructure the ECG signal. Massive experiments show that the method can cancel the base line drift and remarkably reduce the degree of distortion of low frequency component, which provide a better foundation to analyze P and T waves. Then the methods of extracting ECG characteristic information are principally researched. Two algorithms identifying ECG characteristic waves which maturely applied in ECG analyzer are analyzed and improved, and validating experiments are done. An in-depth research on the principle of wavelet transform detecting singular point and some algorithms identifying ECG characteristic waves based on wavelet are also researched and evaluated. Then author innovatively presents the united algorithm based on wavelet to identify and locate the ECG characteristic waves. The principle is: the shapes of transformed ECG characteristic waves are classified first, and then the shapes of detected characteristic waves and peaks are determined by the number of modulus maximum pairs produced by transformed ECG signal at specified scale. Finally the onset and offset of characteristic wave will be determined by amplitude and slope criterion. The differentia with common wavelet algorithm is that the united algorithm first determines wave shape and second locates onset and offset, and then different methods are applied in response to different shapes. The precision to locate ECG characteristic waves, especially to P and T waves, is greatly improved by the algorithm. For a long time it is difficult to identify and locate the low amplitude and multi-shape ECG characteristic waves like P and T wave, Using the algorithm can solve the problem. Massive comparison experiments have been down, which shows that the algorithm is exact and reliable. Arrhythmias and heart rate variability (HRV) have been researched deeply in this thesis. Some familiar arrhythmias have been sum up, and seven criterions have been put forward. HRV is researched from time and frequency domain separately. Power spectrum density of HRV signal is analyzed from time-frequency domain using three-dimensional “color spectrum”. The innovative method can directly reflect the energy value of HRV signal at some time and some frequency, which provides for
doctors and researchers more information to analyze ECG signal. The developing technique of virtual instrument (VI) has been research. Component technique is introduced to develop VI, and the developing method of VI based on component technique is presented, which can effectively improve the speed and quality of VI development. Based on above research achievements, author develops the virtually automatic ECG analyzer. The instrument has not only all functions of hardware-based ECG analyzer, but also has richer function, smarter measurement and lower price. Experiments with clinic datum show that compared with results from manual detection the results is validate and steady , and error is within clinic permit. So it can fulfill the clinic requirement. Data-base management function is also developed in the ECG analyzer, which can provide more information and datum for researching and diagnosing cardiovascular disease. Lastly the next research works are presented at the end of the thesis.
引文
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