基于层次因子图的心电图自动诊断方法研究
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摘要
心电图是心脏生物电活动随时间变化的记录,是临床上诊断心血管疾病的重要工具之一。研究心电图自动诊断,对于提高心电图诊断的正确性、实时性,减轻医务人员的劳动强度,具有十分重要的意义。在分析心电图诊断问题特点及现有方法缺陷的基础上,本文提出了一种基于层次因子图的心电图自动诊断方法,并围绕层次因子图的基本理论,心电图自动诊断层次因子图的建模与推理,以及心电图自动诊断中的心电信号预处理、特征提取、疾病诊断等关键问题展开研究。
论文主要工作及研究成果总结如下:
(1)提出一种新的知识表示和处理模型——层次因子图,并对层次因子图的推理和建模方法进行了研究。层次因子图是标准因子图的扩展模型,它不但具备现有概率图模型的各种优良特性,还具有如下重要特点:①模型采用局部函数对问题域中变量之间的关系进行描述,不但能够表达更多的独立性关系,还能将各种不同类型的知识纳入到一个整体框架中统一处理;②模型采用层次式拓扑结构表达问题领域的结构化知识,为实现复杂系统的层次化建模与推理提供了一条方便有效的途径。层次因子图的推理可通过将层次因子图转化为标准的因子图,再应用因子图推理方法进行实现。论文给出了层次因子图转化为因子图的具体方法,并在研究现有因子图推理算法的基础上,提出了基于和积算法的任意多变量边缘函数计算方法,用于层次因子图推理的实现。
(2)对心电图自动诊断层次因子图的建模与推理方法进行了研究。首先,针对心电图自动诊断问题的特点,提出领域知识与样本数据相结合的建模策略,并采用自顶向下的分级建造方法,进行心电图自动诊断层次因子图的构建。然后,以心电图自动诊断流程为基础,对心电图自动诊断所涉及主要变量及变量之间的关系进行分析,由此确定心电图自动诊断层次因子图的基本拓扑结构,并给出心电图自动诊断层次因子图中局部函数的确定方法。最后,依据心电图自动诊断层次因子图的基本拓扑结构,对心电图自动诊断层次因子图推理过程进行分析,确定心电图自动诊断层次因子图推理的主要任务及实现方法。
(3)对基于噪声层次因子图的心电信号预处理方法进行了研究。以构建噪声层次因子图为目标,对心电信号预处理所涉及变量及变量间关系进行分析,确定了心电信号预处理的主要任务。重点研究了形态学滤波方法在ECG信号预处理中的应用,提出了一种基于多种形态滤波运算的ECG信号基线校正算法,和一种基于形态学滤波和自适应阈值的肌电干扰消除算法。基于本文提出的心电信号预处理方法,构建了噪声层次因子图,完成推理了所需相关消息的计算。
(4)对基于特征层次因子图的心电图特征提取方法进行了研究。以构建特征层次因子图为目标,分析并确定了心电图特征提取的主要任务。重点研究了心电图中QRS波群、ST-T段和P波的检测、定位及特征提取方法:对于QRS波群,提出了一种带反馈修正的多结构元形态学QRS波检测算法,和一种基于曲线分析的QRS波群特征点定位及形态自动分析算法;对于ST-T段,提出了一种基于局部距离变换的T波特征点定位算法,和一种ST段形态分析方法;对于P波,提出了一种基于位置估计和识别后处理的P波检测定位算法。基于本文提出的心电图特征提取方法,构建了特征层次因子图,完成了推理所需相关消息的计算。
(5)对基于疾病层次因子图的心电图疾病诊断方法进行了研究。针对心电图可诊断疾病类别的复杂性特点,提出了基于分解思想的疾病层次因子图建模与求解策略。分别研究了心电图疾病诊断的知识获取方法,变量及其取值确定方法,疾病层次因子图拓扑结构与局部函数的确定方法,具体构建了正常心电图和心肌梗死心电图诊断所需的疾病层次因子图。分析并确定了疾病层次因子图中相关消息计算以及心电图自动诊断因子图推理实现的方法。最后,采用PTB诊断心电数据库真实心电记录作为诊断实例进行验证,结果表明,本文提出的基于层次因子图的心电图自动诊断方法能够正确解决正常心电图诊断、心肌梗死心电图诊断等心电图诊断问题,为心电图自动诊断的真正实现提供了一条有效的途径。
Electrocardiogram (ECG) is the record of variation of bioelectric potential with respect to time as heart beats. It is one of the most important tools for diagnosis of cardiovascular diseases in clinical applications. Research of automated ECG diagnosis is significant for improving quality of accuracy and real time of ECG diagnosis and reducing labour intensity of physicians. On the basis of analysis of the characteristics of ECG diagnosis problem and the limitations of existing methods, a novel method based on hierarchical factor graph (HFG) is proposed for automated ECG diagnosis in this thesis. And works focus on several critical issues, including basic theory of HFG, HFG construction and inference for automated ECG diagnosis, and ECG signal preprocessing, feature extraction and diagnosis during the course of automated ECG diagnosis.
The main contributions of this thesis are summarized as follow:
(1) A novel model for knowledge representation and processing, namely, hierarchical factor graph, is proposed. Methodologies for HFG inference and construction are also studied. HFG is an extention of stardard factor graph. It has all merits of existing probabilistic graphical models and important characteristics as follows: First, local functions are introduced to describe relationships among variables in problem domain, so that more independence relationships can be represented in this model and different kinds of knowledge can be brought into an entire framework and dealt with uniformly. Second, hierarchical structure is introduced in this model to represent structural knowledge in problem domain, which provides a convenient and effective approach for modeling and inferring of complex system hierarchically. HFG inference can be performed by transforming HFG to standard factor graph and applying inference algorithms of factor graph. Methods for transforming HFGs to factor graphs are presented in this thesis. On the basis of study on the exiting inference algorithms of factor graph, a approach based on sum-product algorithm is proposed for computing marginal functions of arbitrary multi-variables in HFG inference.
(2) The methodologies of HFG construction and inference for automated ECG diagnosis are studied. Firstly, according to the characteristics of ECG diagnosis, a strategy for model construction with combination of domain knowledge and sample data is proposed and hierarchical construction method based on top-down idea is adopted to construct HFG for automated ECG diagnosis. Then, on the basis of flow of automated ECG diagnosis, the basic structure of HFG for automated ECG diagnosis is identified by analyzing the main variables in automated ECG diagnosis and the relationships among them. Methods to specify local functions in HFG for automated ECG diagnosis are also proposed. Finally, main tasks and plans to perform inference in HFG for automated ECG diagnosis are identified by analyzing the process of inference according to the basic structure of HFG for automated ECG diagnosis.
(3) Noise HFG based methods are studied for ECG signal preprocessing. With the goal of constructing noise HFG, the variables and their relationships are analyzed, and main tasks are identified in ECG signal preprocessing. Then, studies focus on the application of morphological filter methods to ECG signal preprocessing. A method based on multi morphological filter operations is presented for removing baseline drift in ECG signal. Another method based on morphological operations and adaptive threshold is presented for denoising electromyogram noise in ECG signal. Noise HFG is constructed based on the proposed methods for ECG signal preprocessing and the messages involved in inference are computed.
(4) Feature HFG based methods are studied for ECG feature extraction. Main tasks in ECG feature extraction are analyzed and identified according to the goal of constructing feature HFG. Feature extraction methods for QRS complex, ST-T segment, and P wave are studied respectively. For QRS complex, a multi-structuring-element morphological approach with feedback correction is brought forward for QRS detection, and a method based on curve analysis is proposed for characteristic point location and morphological automatic analysis. For ST-T segment, a method for characteristic point location of T wave based on local distance transform and a method for ST segment shape analysis are proposed. For P wave, a novel method based on location estimation and recognition post-processing is proposed for P wave detection and location. Feature HFG is constructed based on the proposed methods for ECG feature extraction and the messages involved in inference are computed.
(5) Disease HFG based methods are studied for ECG disease diagnosis. Due to the complexity of diseases, a strategy based on decomposition idea is presented for model construction and problem resolution of disease HFG. Methods are studied respectively for knowledge acquisition, identification of variables and their values, and identification of topological structure and local functions of disease HFG. And disease HFGs for normal ECG diagnosis and myocardial infarction ECG diagnosis are constructed completely. Methods are discussed and determined for computing messages in Feature HFG and performing inference in HFG for automated ECG diagnosis. Finally, real ECG records of PTB diagnostic ECG database are utilized as diagnostic instances to validate the proposed models and methods in this thesis. Experimental results show that the automated ECG diagnosis method based on HFG proposed in this thesis can solve ECG diagnosis problem correctly and efficiently.
论文主要工作及研究成果总结如下:
(1)提出一种新的知识表示和处理模型——层次因子图,并对层次因子图的推理和建模方法进行了研究。层次因子图是标准因子图的扩展模型,它不但具备现有概率图模型的各种优良特性,还具有如下重要特点:①模型采用局部函数对问题域中变量之间的关系进行描述,不但能够表达更多的独立性关系,还能将各种不同类型的知识纳入到一个整体框架中统一处理;②模型采用层次式拓扑结构表达问题领域的结构化知识,为实现复杂系统的层次化建模与推理提供了一条方便有效的途径。层次因子图的推理可通过将层次因子图转化为标准的因子图,再应用因子图推理方法进行实现。论文给出了层次因子图转化为因子图的具体方法,并在研究现有因子图推理算法的基础上,提出了基于和积算法的任意多变量边缘函数计算方法,用于层次因子图推理的实现。
(2)对心电图自动诊断层次因子图的建模与推理方法进行了研究。首先,针对心电图自动诊断问题的特点,提出领域知识与样本数据相结合的建模策略,并采用自顶向下的分级建造方法,进行心电图自动诊断层次因子图的构建。然后,以心电图自动诊断流程为基础,对心电图自动诊断所涉及主要变量及变量之间的关系进行分析,由此确定心电图自动诊断层次因子图的基本拓扑结构,并给出心电图自动诊断层次因子图中局部函数的确定方法。最后,依据心电图自动诊断层次因子图的基本拓扑结构,对心电图自动诊断层次因子图推理过程进行分析,确定心电图自动诊断层次因子图推理的主要任务及实现方法。
(3)对基于噪声层次因子图的心电信号预处理方法进行了研究。以构建噪声层次因子图为目标,对心电信号预处理所涉及变量及变量间关系进行分析,确定了心电信号预处理的主要任务。重点研究了形态学滤波方法在ECG信号预处理中的应用,提出了一种基于多种形态滤波运算的ECG信号基线校正算法,和一种基于形态学滤波和自适应阈值的肌电干扰消除算法。基于本文提出的心电信号预处理方法,构建了噪声层次因子图,完成推理了所需相关消息的计算。
(4)对基于特征层次因子图的心电图特征提取方法进行了研究。以构建特征层次因子图为目标,分析并确定了心电图特征提取的主要任务。重点研究了心电图中QRS波群、ST-T段和P波的检测、定位及特征提取方法:对于QRS波群,提出了一种带反馈修正的多结构元形态学QRS波检测算法,和一种基于曲线分析的QRS波群特征点定位及形态自动分析算法;对于ST-T段,提出了一种基于局部距离变换的T波特征点定位算法,和一种ST段形态分析方法;对于P波,提出了一种基于位置估计和识别后处理的P波检测定位算法。基于本文提出的心电图特征提取方法,构建了特征层次因子图,完成了推理所需相关消息的计算。
(5)对基于疾病层次因子图的心电图疾病诊断方法进行了研究。针对心电图可诊断疾病类别的复杂性特点,提出了基于分解思想的疾病层次因子图建模与求解策略。分别研究了心电图疾病诊断的知识获取方法,变量及其取值确定方法,疾病层次因子图拓扑结构与局部函数的确定方法,具体构建了正常心电图和心肌梗死心电图诊断所需的疾病层次因子图。分析并确定了疾病层次因子图中相关消息计算以及心电图自动诊断因子图推理实现的方法。最后,采用PTB诊断心电数据库真实心电记录作为诊断实例进行验证,结果表明,本文提出的基于层次因子图的心电图自动诊断方法能够正确解决正常心电图诊断、心肌梗死心电图诊断等心电图诊断问题,为心电图自动诊断的真正实现提供了一条有效的途径。
Electrocardiogram (ECG) is the record of variation of bioelectric potential with respect to time as heart beats. It is one of the most important tools for diagnosis of cardiovascular diseases in clinical applications. Research of automated ECG diagnosis is significant for improving quality of accuracy and real time of ECG diagnosis and reducing labour intensity of physicians. On the basis of analysis of the characteristics of ECG diagnosis problem and the limitations of existing methods, a novel method based on hierarchical factor graph (HFG) is proposed for automated ECG diagnosis in this thesis. And works focus on several critical issues, including basic theory of HFG, HFG construction and inference for automated ECG diagnosis, and ECG signal preprocessing, feature extraction and diagnosis during the course of automated ECG diagnosis.
The main contributions of this thesis are summarized as follow:
(1) A novel model for knowledge representation and processing, namely, hierarchical factor graph, is proposed. Methodologies for HFG inference and construction are also studied. HFG is an extention of stardard factor graph. It has all merits of existing probabilistic graphical models and important characteristics as follows: First, local functions are introduced to describe relationships among variables in problem domain, so that more independence relationships can be represented in this model and different kinds of knowledge can be brought into an entire framework and dealt with uniformly. Second, hierarchical structure is introduced in this model to represent structural knowledge in problem domain, which provides a convenient and effective approach for modeling and inferring of complex system hierarchically. HFG inference can be performed by transforming HFG to standard factor graph and applying inference algorithms of factor graph. Methods for transforming HFGs to factor graphs are presented in this thesis. On the basis of study on the exiting inference algorithms of factor graph, a approach based on sum-product algorithm is proposed for computing marginal functions of arbitrary multi-variables in HFG inference.
(2) The methodologies of HFG construction and inference for automated ECG diagnosis are studied. Firstly, according to the characteristics of ECG diagnosis, a strategy for model construction with combination of domain knowledge and sample data is proposed and hierarchical construction method based on top-down idea is adopted to construct HFG for automated ECG diagnosis. Then, on the basis of flow of automated ECG diagnosis, the basic structure of HFG for automated ECG diagnosis is identified by analyzing the main variables in automated ECG diagnosis and the relationships among them. Methods to specify local functions in HFG for automated ECG diagnosis are also proposed. Finally, main tasks and plans to perform inference in HFG for automated ECG diagnosis are identified by analyzing the process of inference according to the basic structure of HFG for automated ECG diagnosis.
(3) Noise HFG based methods are studied for ECG signal preprocessing. With the goal of constructing noise HFG, the variables and their relationships are analyzed, and main tasks are identified in ECG signal preprocessing. Then, studies focus on the application of morphological filter methods to ECG signal preprocessing. A method based on multi morphological filter operations is presented for removing baseline drift in ECG signal. Another method based on morphological operations and adaptive threshold is presented for denoising electromyogram noise in ECG signal. Noise HFG is constructed based on the proposed methods for ECG signal preprocessing and the messages involved in inference are computed.
(4) Feature HFG based methods are studied for ECG feature extraction. Main tasks in ECG feature extraction are analyzed and identified according to the goal of constructing feature HFG. Feature extraction methods for QRS complex, ST-T segment, and P wave are studied respectively. For QRS complex, a multi-structuring-element morphological approach with feedback correction is brought forward for QRS detection, and a method based on curve analysis is proposed for characteristic point location and morphological automatic analysis. For ST-T segment, a method for characteristic point location of T wave based on local distance transform and a method for ST segment shape analysis are proposed. For P wave, a novel method based on location estimation and recognition post-processing is proposed for P wave detection and location. Feature HFG is constructed based on the proposed methods for ECG feature extraction and the messages involved in inference are computed.
(5) Disease HFG based methods are studied for ECG disease diagnosis. Due to the complexity of diseases, a strategy based on decomposition idea is presented for model construction and problem resolution of disease HFG. Methods are studied respectively for knowledge acquisition, identification of variables and their values, and identification of topological structure and local functions of disease HFG. And disease HFGs for normal ECG diagnosis and myocardial infarction ECG diagnosis are constructed completely. Methods are discussed and determined for computing messages in Feature HFG and performing inference in HFG for automated ECG diagnosis. Finally, real ECG records of PTB diagnostic ECG database are utilized as diagnostic instances to validate the proposed models and methods in this thesis. Experimental results show that the automated ECG diagnosis method based on HFG proposed in this thesis can solve ECG diagnosis problem correctly and efficiently.
引文
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