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Multi-lead ECG signal analysis for myocardial infarction detection and localization through the mapping of Grassmannian and Euclidean features into a common Hilbert space

机译：通过将Grassmannian和Euclidean特征映射到公共Hilbert空间来进行心肌梗塞检测和定位的多导联ECG信号分析

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Background and objective: Electrocardiogram is commonly used as a diagnostic tool for the monitoring of cardiac health and the detection of possible heart diseases. However, the procedure followed for the diagnosis of heart abnormalities is time consuming and prone to human errors. Thus, the development of computer-aided techniques for the automatic analysis of electrocardiogram signals is of vital importance for the diagnosis and prevention of heart diseases. The most serious outcome of coronary heart disease is the myocardial infarction, i.e., the rapid and irreversible damage of cardiac muscles, which, if not diagnosed and treated in time, continues to damage further the myocardial structure and function. In this paper we propose a novel approach for the automatic detection and localization of myocardial infarction from multi-lead electrocardiogram signals.Methods: The proposed method initially reshapes the multidimensional signal into a third-order tensor structure and subsequently extracts feature representations in both Euclidean and Grassmannian space. In addition, two different methods are proposed for the mapping of the two different feature representations into a common Hilbert space before the final classification of signals. The first approach is based on the mapping of both Grassmannian and Euclidean features in a Reproducing Kernel Hilbert Space (RKHS), while the second one attempts to initially apply Vector of Locally Aggregated Descriptors (VLAD) encoding directly to Grassmann manifold and then concatenate the two VLAD representations.Results: For the evaluation of the proposed method, we have conducted extensive tests using a publicly available dataset, namely PTB Diagnostic ECG database, containing 549 multi-lead ECG data recordings from 290 subjects and from different diagnostic classes. The method provides an excellent detection rate of 100%, and localization rate, i.e., 100% with the first fusion method and 99.7% with the second one.Conclusions: The Experimental results presented in this paper show the superiority of the proposed methodology against a number of state-of-the-art approaches. The main advantage of the proposed approach is that it exploits better the intercorrelations between signals of different ECG leads, by extracting feature representations that lie in different geometrical spaces and contain complementary information with regard to the dynamics of signals. (C) 2019 Elsevier Ltd. All rights reserved.
机译：背景与目的：心电图通常用作诊断工具，用于监测心脏健康和发现可能的心脏病。但是，用于诊断心脏异常的程序耗时且容易发生人为错误。因此，开发用于自动分析心电图信号的计算机辅助技术对于心脏病的诊断和预防至关重要。冠心病的最严重后果是心肌梗塞，即心肌的快速和不可逆转的损害，如果不及时诊断和治疗，会继续损害心肌的结构和功能。在本文中，我们提出了一种从多导联心电图信号自动检测和定位心肌梗塞的新方法。方法：该方法首先将多维信号重塑为三阶张量结构，然后提取欧几里得和二维中的特征表示格拉斯曼空间。另外，提出了两种不同的方法，用于在信号的最终分类之前将两种不同的特征表示映射到公共希尔伯特空间。第一种方法基于再现内核希尔伯特空间（RKHS）中Grassmannian和Euclidean特征的映射，而第二种方法则尝试将最初的局部聚集描述符矢量（VLAD）编码直接应用到Grassmann流形，然后将两者串联结果：为了评估所提出的方法，我们使用了一个公共数据集，即PTB Diagnostic ECG数据库，进行了广泛的测试，该数据库包含来自290位受试者和不同诊断类别的549条多导联ECG数据记录。该方法具有出色的100％检测率和定位率，即第一种融合方法的定位率为100％，第二种融合方法的定位率为99.7％。结论：本文给出的实验结果表明，该方法相对于a融合方法具有优越性。最先进的方法。所提出的方法的主要优点是，通过提取位于不同几何空间中并包含有关信号动力学的补充信息的特征表示，可以更好地利用不同ECG导线信号之间的互相关性。 （C）2019 Elsevier Ltd.保留所有权利。

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