心电动力学信号数据特征提取与模式识别的研究进展
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摘要
现代控制理论和计算机技术的进步,为深入研究心电信号内在动态病理特征创造了良好条件,并推动了相关学者在基于心电信号转化数据的神经网络模型构建、心电信号异质度特征的量化指标提取、心脏疾病辅助诊断等方面的早期探索。本文综述了心电动力学信号数据特征的研究现状,并介绍了心电动力学信号数据特征的提取方法、模式识别及其在心肌缺血辅助诊断、心肌缺血引发心脏疾病早期筛查和疗效评估等方面的应用。
With the development of modern control theory and computer technology,good conditions have been prepared for further research on the dynamic pathological features inherently in ECG signals. It also promotes early studies on neural network modeling of ECG signals,extraction of quantitative indicators characterizing the heterogeneity of ECG signals,auxiliary diagnosis of cardiac diseases,etc. This paper reviews literatures of the previous research on the data feature of cardiodynamicsgram signals,and introduces its extraction method,pattern recognition and its application in the auxiliary diagnosis of myocardial ischemia,early screening of cardiac diseases resulting from myocardial ischemia,evaluation of curative effect and so on.
With the development of modern control theory and computer technology,good conditions have been prepared for further research on the dynamic pathological features inherently in ECG signals. It also promotes early studies on neural network modeling of ECG signals,extraction of quantitative indicators characterizing the heterogeneity of ECG signals,auxiliary diagnosis of cardiac diseases,etc. This paper reviews literatures of the previous research on the data feature of cardiodynamicsgram signals,and introduces its extraction method,pattern recognition and its application in the auxiliary diagnosis of myocardial ischemia,early screening of cardiac diseases resulting from myocardial ischemia,evaluation of curative effect and so on.
引文
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[2]Dower GE,Yakush A,Nazzal SB,et al.Deriving the 12-lead electrocardiogram from four(EASI)electrodes[J].JElectrocardiol,1988,21(Suppl):S182-S187.
[3]Ghadrdoost B,Haghjoo M,Firouzi A.Accuracy of cardiogoniometry compared with electrocardiography in the diagnosis of coronary artery disease[J].Res Cardiovasc Med,2015,4(1):1-5.
[4]Birkemeyer R,Toelg R,Zeymer U,et al.Comparison of cardiogoniometry and electrocardiography with perfusion cardiac magnetic resonance imaging and late gadolinium enhancement[J].Europace,2012,14(12):1793-1798.
[5]de Luna AB.Basic electrocardiography:normal and abnormal ECG patterns[M].Malden:Blackwell Publishing,2008.
[6]Go AS,Mozaffarian D,Roger VL,et al.Heart disease and stroke statistics-2013 update:a report from the American Heart Association[J].Circulation,2013,127(1):e6-e245.
[7]Ince T,Kiranyaz S,Gabbouj M.A generic and robust system for automated patient-specific classification of ECG signals[J].IEEE Trans Biomed Eng,2009,56(5):1415-1426.
[8]王聪,陈填锐,刘腾飞.确定学习与基于数据的建模及控制[J].自动化学报,2009,35(6):693-706.
[9]Wang C,Dong XD,Ou SX,et al.A new method for early detection of myocardial ischemia:cardiodynamicsgram(CDG)[J].Sci China:Inform Sci,2016,59(1):1-11.
[10]Dong XD,Wang C,Si WJ.ECG beat classification via deterministic learning[J].Neurocomputing,2017,240(5):1-12.
[11]文斯民.基于确定学习的心肌缺血早期诊断系统的配套信息管理系统的开发[D].广州:华南理工大学,2016.
[12]王聪,吴伟明,邓木清,等.一种心电动力学数据量化分析方法:CN107260161A[P].2017-10-20.
[13]龚莹岚,张鞠成,温煦,等.运动训练负荷监测方法:CN108992061A[P].2018-12-14.
[14]Wen X,Guo B,Gong Y,et al.Cardiodynamicsgram:a novel tool for monitoring cardiac function in exercise training[J].J Sports Sci,2018,36(22):2583-2587.
[15]焦方惠,郭永新,焦青.基于近似熵的不同状态心电信号的分析[J].中国医疗器械杂志,2012,36(1):19-21.
[16]苏嘉豪,徐维超.基于香农信息熵的心电R波检测算法[J].电子世界,2017,24(8):59-61,64.
[17]周卫,伍银波.一种基于动态模式识别的心电信号ST-T段识别方法:CN106815570A[P].2017-06-09.
[18]Wang C,Dong XD,Hu JM,et al.ECG pattern modeling and recognition via deterministic learning[J].Control Theory Tech,2014,12(4):333-344.
[19]雷鹏.基于心电动力学图的远程心肌缺血检测系统的设计与实现[D].广州:华南理工大学,2017.
[20]Gautam M,Giri V.A comprehensive analysis for ECGclassification[J].AEJSR,2017,12(1):47-53.