Multiscale Recurrence Quantification Analysis of Spatial Cardiac Vectorcardiogram Signals
Multiscale Recurrence Quantification Analysis of Spatial Cardiac Vectorcardiogram Signals
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DOI：
10.1109/tbme.2010.2063704
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发表时间：
2011-02-01
影响因子：
4.6
通讯作者：
Yang, Hui
中科院分区：
文献类型：
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作者：
Yang, Hui
心肌梗死（MI），也被称为心脏病发作，是世界上导致死亡的主要原因。在体表记录空间矢量心动图（VCG）信号，监测体表正、横、矢状面三个正交方向的潜在心电活动。三维VCG矢量环路为研究心脏动力学行为提供了一种新的途径，而不是传统的单心电迹线的延时重建相空间。然而，很少，如果有的话，以前的方法研究心脏疾病和VCG信号复发模式之间的关系。本文提出了多小波尺度VCG信号的递归量化分析（RQA），用于心脏疾病的识别。在PhysioNet Physikalisch-Technische Bundesanstalt数据库的随机分类实验中，使用多尺度RQA特征的线性分类模型检测MI的平均灵敏度为96.5%，平均特异性为75%，与人类专家的表现相当。这项研究强烈表明，潜在的自动心肌梗死分类算法的诊断和治疗目的。
Myocardial infarction (MI), also known as a heart attack, is a leading cause of mortality in the world. Spatial vectorcardiogram (VCG) signals are recorded on the body surface to monitor the underlying cardiac electrical activities in three orthogonal directions of the body, namely, frontal, transverse, and sagittal planes. The 3-D VCG vector loops provide a new way to study the cardiac dynamical behaviors, as opposed to the conventional time-delay reconstructed phase space from a single ECG trace. However, few, if any, previous approaches studied the relationships between cardiac disorders and recurrence patterns in VCG signals. This paper presents the recurrence quantification analysis (RQA) of VCG signals in multiple wavelet scales for the identification of cardiac disorders. The linear classification models using multiscale RQA features were shown to detect MI with an average sensitivity of 96.5% and an average specificity of 75% in the randomized classification experiments of PhysioNet Physikalisch-Technische Bundesanstalt database, which is comparable to the performance of human experts. This study is strongly indicative of potential automated MI classification algorithms for diagnostic and therapeutic purposes.