A Weighted SVM Based Approach for Automatic Detection of Posterior Myocardial Infarction Using VCG Signals

Eedara Prabhakararao, S. Dandapat
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引用次数: 17

Abstract

Myocardial infarction (MI), commonly known as heart attack is a life-threatening arrhythmia occurs due to insufficient oxygen supply to the heart tissues resulted from formation of clots in one or more coronary arteries. There is a growing interest among researchers for automatic detection of MI using computer algorithms. Based on the spatial location of damaged tissues MI is further categorized as anterior MI, septal MI, lateral MI, inferior MI and posterior MI. Among all, automatic detection of posterior MI (PMI) with standard 12-lead electrocardiogram (12-lead ECG) signal is challenging as it does not have monitoring electrodes posterior to human body. In this paper, we propose an automatic method for PMI detection using 3-lead vectorcardiogram (3-lead VCG) signal. The proposed approach exploits changes in electrical conduction properties of heart tissues during cardiac activity for healthy control (HC) and PMI subjects in three-dimensional (3D) space. To quantify these changes multiscale eigen features (MSEF) of subband matrices are used. Furthermore, we propose a cost sensitive weighted support vector machine (WSVM) classifier to combat class imbalance, which is a common problem in real-world disease data classification. The publicly available PhysioNet/PTBDB diagnostic database has been used to validate the proposed method by using a total of 1463 HC, and 148 PMI 4 sec 3-lead VCG signals. The best test accuracy of 96.69%, sensitivity of 80%, and geometric mean of 88.72% are achieved by WSVM classifier with radial basis function (RBF) kernel.
基于加权支持向量机的后置心肌梗死VCG信号自动检测方法
心肌梗死(MI),俗称心脏病发作,是由于一个或多个冠状动脉形成血栓导致心脏组织供氧不足而发生的危及生命的心律失常。研究人员对使用计算机算法自动检测心肌梗死越来越感兴趣。根据损伤组织的空间位置,心肌梗死又分为前路心肌梗死、间隔心肌梗死、外侧心肌梗死、下路心肌梗死和后路心肌梗死。其中,使用标准12导联心电图(12导联ECG)信号自动检测后路心肌梗死(PMI)具有挑战性,因为它没有在人体后方的监测电极。在本文中,我们提出了一种利用3导联矢量心电图(3导联VCG)信号自动检测PMI的方法。该方法利用健康对照(HC)和PMI受试者在三维(3D)空间中心脏活动期间心脏组织电传导特性的变化。为了量化这些变化,使用了子带矩阵的多尺度特征(MSEF)。此外,我们提出了一种代价敏感加权支持向量机(WSVM)分类器来解决现实世界疾病数据分类中常见的类不平衡问题。公开可用的PhysioNet/PTBDB诊断数据库通过使用1463个HC和148个PMI 4秒3导联VCG信号来验证所提出的方法。采用径向基函数(RBF)核的WSVM分类器的检测准确率为96.69%,灵敏度为80%,几何平均值为88.72%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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