Zhang Jieshuo, Liu Ming, Xiong Peng, Du Haiman, Yang Jianli, Xu Jinpeng, Hou Zengguang, Liu Xiuling
IEEE Trans Biomed Eng. 2023 Mar;70(3):812-823. doi: 10.1109/TBME.2022.3202962. Epub 2023 Feb 17.
Myocardial infarction (MI) causes rapid and permanent damage to the heart muscle. Therefore, it can deteriorate the myocardial structure and function if not timely diagnosed and treated. However, it is difficult to determine the precise localization of MI based on vectorcardiogram (VCG) due to the existing studies ignore the spatiotemporal features of VCG.
In this paper, a precise MI localization method was proposed based on Tucker decomposition. The multi-scale characteristics of wavelet transform and the spatiotemporal characteristics of VCG were used to construct the VCG tensor containing the local and the spatiotemporal information. The VCG tensor was compressed in the time dimension based on Tucker decomposition to remove redundant information and extract the local spatiotemporal features. The features were fed back to the TreeBagger classifier.
The proposed method achieved a total accuracy of 99.80% for 11 types of MI on the benchmark Physikalisch-Technische Bundesanstalt database. The area under the receiver operating characteristic curves and precision-recall curves of each kind of VCG signal was more than 0.88.
The proposed algorithm effectively realized the classification of normal and 11 categories of MI using VCG.
Therefore, this study provides new ideas for the intelligent diagnosis of MI based on VCG.
心肌梗死（MI）会对心肌造成快速且永久性的损伤。因此，如果不及时诊断和治疗，会使心肌结构和功能恶化。然而，由于现有研究忽略了心电向量图（VCG）的时空特征，基于VCG来确定MI的精确位置很困难。
本文提出了一种基于塔克分解的精确MI定位方法。利用小波变换的多尺度特征和VCG的时空特征来构建包含局部和时空信息的VCG张量。基于塔克分解在时间维度上对VCG张量进行压缩，以去除冗余信息并提取局部时空特征。将这些特征反馈给TreeBagger分类器。
在基准德国联邦物理技术研究院数据库上，该方法对11种类型的MI实现了99.80%的总准确率。每种VCG信号的受试者工作特征曲线和精确率-召回率曲线下面积均大于0.88。
所提算法有效地实现了基于VCG对正常情况和11类MI的分类。
因此，本研究为基于VCG的MI智能诊断提供了新思路。
