Localization of Myocardial Infarction From 2D-VCG Tensor With DSC-Net
文献类型：期刊论文
| 作者 | Xiong, Peng1; Li, Kunlin1; Zhang, Jieshuo1; He, Cong1; Du, Haiman1; Yang, Jianli1; Cao, Xiaohua3; Hou, Zengguang2 ; Liu, Xiuling1
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| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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| 出版日期 | 2023 |
| 卷号 | 72页码:10 |
| ISSN号 | 0018-9456 |
| 关键词 | Index Terms-2-D-vectorcardiogram (2D-VCG) depthwise separable convolutional myocardial infarction (MI) vectorcardiogram (VCG) |
| DOI | 10.1109/TIM.2023.3290966 |
| 通讯作者 | Zhang, Jieshuo(zhangjieshuo@126.com) ; Liu, Xiuling(liuxiuling121@hotmail.com) |
| 英文摘要 | Myocardial infarction (MI) can cause acute and permanent damage to the myocardial muscle. Vectorcardiogram (VCG) is formed by the time-varying coordinates of cardiac electrical activity in space. According to different infarct locations, the ring of VCG in the three orthogonal planes has pathological morphological changes. Yet the existing algorithms only extract the pathological information of three-lead VCG signals, but they do not fully consider the correlation information between different orthogonal planes. We proposed a depthwise separable convolution network (DSC-Net) for automatic MI localization from 2D-VCG tensor. Using the orthogonality between the lead axes, we first combine the three leads in pairs to form a 2D-VCG, and then construct a 2D-VCG tensor that captures the correlation information between leads. DSC-Net extracts spatial features related to MI obtained in 2D-VCG before Softmax is applied to classify MIs. The proposed method was validated on the benchmark Physikalisch Technische-Bundesanstalt dataset, which includes VCG of 11 types of MI. We demonstrated, with the cardiac electrical activity spatial features obtained from the 2D-VCG tensor, that the accuracy of 11 categories of MI and normal is higher than 99.92%. The proposed model effectively realized the localization of MI with competitively high accuracy for all 11 categories. |
| WOS关键词 | VECTORCARDIOGRAM ; DIAGNOSIS ; POSTERIOR |
| 资助项目 | National Natural Science Foundation of China[62276087] ; National Natural Science Foundation of China[U20A20224] ; Natural Science Foundation of Hebei Province[F2022201037] ; Natural Science Foundation of Hebei Province[F2021201008] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:001045579100011 |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54020]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Zhang, Jieshuo; Liu, Xiuling |
| 作者单位 | 1.Hebei Univ, Coll Elect & Informat Engn, Key Lab Digital Med Engn Hebei Prov, Baoding 071002, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Hosp Hebei Univ, Baoding 071002, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiong, Peng,Li, Kunlin,Zhang, Jieshuo,et al. Localization of Myocardial Infarction From 2D-VCG Tensor With DSC-Net[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2023,72:10. |
| APA | Xiong, Peng.,Li, Kunlin.,Zhang, Jieshuo.,He, Cong.,Du, Haiman.,...&Liu, Xiuling.(2023).Localization of Myocardial Infarction From 2D-VCG Tensor With DSC-Net.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,72,10. |
| MLA | Xiong, Peng,et al."Localization of Myocardial Infarction From 2D-VCG Tensor With DSC-Net".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 72(2023):10. |
入库方式： OAI收割
来源：自动化研究所
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