| 基于多路可视图的健康与心梗患者心电图信号复杂网络识别 |
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| 引用本文： | 马志怡,杨小冬,何爱军,马璐,王俊.基于多路可视图的健康与心梗患者心电图信号复杂网络识别[J].物理学报,2022(5):48-57. |
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| 作者姓名： | 马志怡 杨小冬 何爱军 马璐 王俊 |
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| 作者单位： | 中国矿业大学计算机科学与技术学院;南京大学电子科学与工程学院;中国矿业大学信息与控制工程学院;宿州职业技术学院;南京邮电大学地理与生物信息学院 |
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| 基金项目： | 徐州市重点研发计划(社会发展)(批准号:KC21304);国家自然科学基金(批准号:61772532,61876186)资助的课题. |
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| 摘 要： | 可视图(visibility graph, VG)算法已被证明是将时间序列转换为复杂网络的简单且高效的方法,其构成的复杂网络在拓扑结构中继承了原始时间序列的动力学特性.目前,单维时间序列的可视图分析已趋于成熟,但应用于复杂系统时,单变量往往无法描述系统的全局特征.本文提出一种新的多元时间序列分析方法,将心梗和健康人的12导联心电图(electrocardiograph, ECG)信号转换为多路可视图,以每个导联为一个节点,两个导联构成可视图的层间互信息为连边权重,将其映射到复杂网络.由于不同人群的全连通网络表现为完全相同的拓扑结构,无法唯一表征不同个体的动力学特征,根据层间互信息大小重构网络,提取权重度和加权聚类系数,实现对不同人群12导联ECG信号的识别.为判断序列长度对识别效果的影响,引入多尺度权重度分布熵.由于健康受试者拥有更高的平均权重度和平均加权聚类系数,其映射网络表现为更加规则的结构、更高的复杂性和连接性,可以与心梗患者进行区分,两个参数的识别准确率均达到93.3%.
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| 关 键 词： | 心肌梗塞 多元时间序列 多路可视图 复杂网络 |
Complex network recognition of electrocardiograph signals in health and myocardial infarction patients based on multiplex visibility graph |
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| Ma Zhi-Yi,Yang Xiao-Dong,He Ai-Jun,Ma Lu,Wang Jun.Complex network recognition of electrocardiograph signals in health and myocardial infarction patients based on multiplex visibility graph[J].Acta Physica Sinica,2022(5):48-57. |
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| Authors: | Ma Zhi-Yi Yang Xiao-Dong He Ai-Jun Ma Lu Wang Jun |
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| Affiliation: | (School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China;School of Electronic Science and Engineering,Nanjing University,Nanjing 210023,China;School of Intoramtion and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China;Suzhou Vocational and Technical College,Suzhou 234000,China;School of Geographic and Biologic Information,Nanjing University of Posts and Telecommunications,Nanjing 210023,China) |
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| Abstract: | The visibility graph algorithm proves to be a simple and efficient method to transform time series into complex network and has been widely used in time series analysis because it can inherit the dynamic characteristics of original time series in topological structure.Now,visibility graph analysis of univariate time series has become mature gradually.However,most of complex systems in real world are multi-dimensional,so the univariate analysis is difficult to describe the global characteristics when applied to multi-dimensional series.In this paper,a novel method of analyzing the multivariate time series is proposed.For patients with myocardial infarction and healthy subjects,the 12-lead electrocardiogram signals of each individual are considered as a multivariate time series,which is transformed into a multiplex visibility graph through visibility graph algorithm and then mapped to fully connected complex network.Each node of the network corresponds to a lead,and the inter-layer mutual information between visibility graphs of two leads represents the weight of edges.Owing to the fully connected network of different groups showing an identical topological structure,the dynamic characteristics of different individuals cannot be uniquely represented.Therefore,we reconstruct the fully connected network according to inter-layer mutual information,and when the value of inter-layer mutual information is less than the threshold we set,the edge corresponding to the inter-layer mutual information is deleted.We extract average weighted degree and average weighted clustering coefficient of reconstructed networks for recognizing the 12-lead ECG signals of healthy subjects and myocardial infarction patients.Moreover,multiscale weighted distribution entropy is also introduced to analyze the relation between the length of original time series and final recognition result.Owing to higher average weighted degree and average weighted clustering coefficient of healthy subjects,their reconstructed networks show a more regular structure,higher complexity and connectivity,and the healthy subjects can be distinguished from patients with myocardial infarction,whose reconstructed networks are sparser.Experimental results show that the identification accuracy of both parameters,average weighted degree and average weighted clustering coefficient,reaches 93.3%,which can distinguish between the 12-lead electrocardiograph signals of healthy people and patients with myocardial infarction,and realize the automatic detection of myocardial infarction. |
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| Keywords: | myocardial infarction multivariate time series multiplex visibility graph complex network |
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