心电图形态特征的识别及其在分类中的作用研究
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摘要
心电图(Electrocardiogram, ECG)是一种简单、有效、低成本的心脏电位活动的记录与检测技术。将计算机用于心电图的疾病诊断是模式识别领域中的一项经典任务。本文中,将有经验的医生采用的心电图形态特征引入到心电图自动分析的研究中。没有能够有效利用这种特征集合该是目前心电图自动诊断结果不理想的一个重要原因。
标准心电图数据库是一种用于验证心电图特征提取和模式分类算法性能的测试数据库。目前主要有麻省理工学院(MIT)心率失常数据库、QT数据库、欧共体(CSE)多导联数据库和美国心脏协会(AHA)数据库这四种常用的标准数据库。随着采集设备和诊断方法的不断改进,这些数据库已逐步不能适应研究和应用的需要。为了验证面向临床应用的研究结果,构建了中国心血管疾病数据库(Chinese Cardiovascular Disease Database, CCDD or CCD Database)。该数据库包含了的12导联心电图记录、特征标注信息和心拍诊断结果,更重要的是引入了极具诊断价值的形态特征参数。CCDD是本文测试工作的基础。
随后,提出了两种形态特征的识别方法。第一种基于一阶邻近-动态时间规整算法进行设计。依靠这种有效的时间序列相似度比较算法,结合系统模板库中的心电图数据段对比,实现了形态的识别。采用模板选择与压缩方法在原模板库中选择具有代表性的模板实例,减少了模板库中模板的数量,从而提高了算法的处理速度。系统最终运用原模板库中较少比例的模板实例,达到90.7%的识别准确率。在第二种识别方法中,将算法的实时性作为研究的重点。该方法的核心思想是模拟医生识别形态特征的思维过程,并运用动态时间规整算法进行辅助检查,识别准确率达到了91.7%。
针对“为什么要使用形态特征”以及“如何使用形态特征”这两个问题,本文提出了四种形态特征表示方法,并作对比实验。五种经典分类器在使用形态特征与不使用形态特征情况下的实验结果变化表明,形态特征在心电图模式分类中确实起到了积极作用,经过特征选择算法的优化,分类的准确率有了进一步的提高。
心电图形态特征的识别方法需要有待进一步研究和改进,并以其对实际分类效果的改善为最终目标。
The analysis of electrocardiogram (ECG) is a non-invasive, effective, simple and low-cost technique to detect the electrical heart activity. To detect and classify ECG diseases by computer is a classic pattern recognition task. Morphology features, which influence ECG diagnosis results, are introduced according to physicians'experiences and advice. Fail to utilize them should be one of the most important reasons for the underperformance of automatic ECG classification.
Standard ECG databases are created for validating and comparing different algorithms on feature detection and disease classification. At present, there are four frequently used standard databases:MIT-BIH arrhythmia database, QT database, CSE multi-lead database and AHA database. With the development in equipment and diagnosis approach, these databases can not meet the requirements of further R&D works. Therefore Chinese Cardiovascular Disease Database (CCDD or CCD database), which contains 12-Lead ECG data, detailed annotation features and beat diagnosis result is constructed. It is advanced for not only improving the raw ECG data's technical parameters, but also introducing valuable morphology features which are utilized by experienced cardiologists effectively. All test works in this paper are based on CCDD.
After that, two methods are presented for ECG morphology features recognition. The first one is based on 1 nearest neighbor classifier and dynamic time warping (INN-DTW). INN-DTW is a strong time series matching algorithm and used to compare the ECG segments with the templates stored in the system. Template selection and reduction is applied to accelerate the classification speed and cut down the templates volume as well. With the help of new proposed template reduction algorithm, an accuracy of 90.71% is acquired by using a small portion of the original template set. The second one is a real time algorithm focused on higher speed by simulating cardiologists' recognition procedure while using DTW algorithm for double checking. The accuracy of this method is 91.07%.
In order to answer the "why" and "how" challenges of ECG morphological features' real utility, four kinds of representation method are proposed for morphological features, and experiments results are compared on 5 kinds of widely used classifiers between using morphological features and without morphological features. By utilizing feature selection algorithms, the performance is improved once again.
The study of morphology features recognition on ECG should be investigated further to meet the requirements of clinical classification.
标准心电图数据库是一种用于验证心电图特征提取和模式分类算法性能的测试数据库。目前主要有麻省理工学院(MIT)心率失常数据库、QT数据库、欧共体(CSE)多导联数据库和美国心脏协会(AHA)数据库这四种常用的标准数据库。随着采集设备和诊断方法的不断改进,这些数据库已逐步不能适应研究和应用的需要。为了验证面向临床应用的研究结果,构建了中国心血管疾病数据库(Chinese Cardiovascular Disease Database, CCDD or CCD Database)。该数据库包含了的12导联心电图记录、特征标注信息和心拍诊断结果,更重要的是引入了极具诊断价值的形态特征参数。CCDD是本文测试工作的基础。
随后,提出了两种形态特征的识别方法。第一种基于一阶邻近-动态时间规整算法进行设计。依靠这种有效的时间序列相似度比较算法,结合系统模板库中的心电图数据段对比,实现了形态的识别。采用模板选择与压缩方法在原模板库中选择具有代表性的模板实例,减少了模板库中模板的数量,从而提高了算法的处理速度。系统最终运用原模板库中较少比例的模板实例,达到90.7%的识别准确率。在第二种识别方法中,将算法的实时性作为研究的重点。该方法的核心思想是模拟医生识别形态特征的思维过程,并运用动态时间规整算法进行辅助检查,识别准确率达到了91.7%。
针对“为什么要使用形态特征”以及“如何使用形态特征”这两个问题,本文提出了四种形态特征表示方法,并作对比实验。五种经典分类器在使用形态特征与不使用形态特征情况下的实验结果变化表明,形态特征在心电图模式分类中确实起到了积极作用,经过特征选择算法的优化,分类的准确率有了进一步的提高。
心电图形态特征的识别方法需要有待进一步研究和改进,并以其对实际分类效果的改善为最终目标。
The analysis of electrocardiogram (ECG) is a non-invasive, effective, simple and low-cost technique to detect the electrical heart activity. To detect and classify ECG diseases by computer is a classic pattern recognition task. Morphology features, which influence ECG diagnosis results, are introduced according to physicians'experiences and advice. Fail to utilize them should be one of the most important reasons for the underperformance of automatic ECG classification.
Standard ECG databases are created for validating and comparing different algorithms on feature detection and disease classification. At present, there are four frequently used standard databases:MIT-BIH arrhythmia database, QT database, CSE multi-lead database and AHA database. With the development in equipment and diagnosis approach, these databases can not meet the requirements of further R&D works. Therefore Chinese Cardiovascular Disease Database (CCDD or CCD database), which contains 12-Lead ECG data, detailed annotation features and beat diagnosis result is constructed. It is advanced for not only improving the raw ECG data's technical parameters, but also introducing valuable morphology features which are utilized by experienced cardiologists effectively. All test works in this paper are based on CCDD.
After that, two methods are presented for ECG morphology features recognition. The first one is based on 1 nearest neighbor classifier and dynamic time warping (INN-DTW). INN-DTW is a strong time series matching algorithm and used to compare the ECG segments with the templates stored in the system. Template selection and reduction is applied to accelerate the classification speed and cut down the templates volume as well. With the help of new proposed template reduction algorithm, an accuracy of 90.71% is acquired by using a small portion of the original template set. The second one is a real time algorithm focused on higher speed by simulating cardiologists' recognition procedure while using DTW algorithm for double checking. The accuracy of this method is 91.07%.
In order to answer the "why" and "how" challenges of ECG morphological features' real utility, four kinds of representation method are proposed for morphological features, and experiments results are compared on 5 kinds of widely used classifiers between using morphological features and without morphological features. By utilizing feature selection algorithms, the performance is improved once again.
The study of morphology features recognition on ECG should be investigated further to meet the requirements of clinical classification.
引文
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