Detection of inferior myocardial infarction based on morphological characteristics_Journal of Biomedical Engineering

Authors：

XIONG Peng , QI Mingrui , ZHANG Jieshuo , LIU Ming , HOU Zengguang ,  WANG Hongrui ,  LIU Xiuling

Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China;

Corresponding author：

WANG Hongrui, Email: hongrui@hbu.edu.cn; LIU Xiuling, Email: liuxiuling121@hotmail.com

Keywords：

inferior myocardial infarction; electrocardiogram; morphological characteristics; genetic algorithm

DOI：

10.7507/1001-5515.202001027

Video：

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Abstract Full text Figures/Tables Video References Cited by

Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.

Citation： XIONG Peng, QI Mingrui, ZHANG Jieshuo, LIU Ming, HOU Zengguang, WANG Hongrui, LIU Xiuling. Detection of inferior myocardial infarction based on morphological characteristics. Journal of Biomedical Engineering, 2021, 38(1): 65-71. doi: 10.7507/1001-5515.202001027 Copy

1.	胡盛寿, 高润霖, 刘力生, 等. 《中国心血管病报告2018》概要. 中国循环杂志, 2019, 34(03): 209-220.
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12.	Acharya U R, Fujita H, Sudarshan V K, et al. Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl-Based Syst, 2016, 99(1): 146-156.
13.	Sharma L D, Sunkaria R K. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image Video Process, 2017, 12(2): 199-206.
14.	Goldberger A L, Amaral L A N, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): e215-e220.
15.	刘秀玲, 乔磊, 杨建利, 等. 改进小波阈值法用于心电信号去噪. 生物医学工程学杂志, 2014, 31(3): 511-515.
16.	Smith S. 急性心肌梗死心电图快速判读: 再灌注治疗循环手册. 天津: 天津科技翻译出版公司, 2004: 139-149.
17.	李洪奇. 急性心肌梗死心电图诊断分析. 世界最新医学信息文摘, 2017, 17(28): 146-148.
18.	Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, 1985, 32(3): 230-236.
19.	李露. 心电信号ST段分析研究. 重庆: 重庆大学, 2007.
20.	辛宇, 童孟军, 华宇婷. 一种基于最优特征选择改进的遗传算法. 传感技术学报, 2018, 31(11): 131-136.
21.	田江华, 卢广平, 张海澄, 等. 体表心电图在急性下壁心肌梗死中的定位诊断价值. 中国心血管杂志, 2018, 23(6): 477-480.
22.	He K, Sun J. Convolutional neural networks at constrained time cost// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015: 5353-5360.

1. 胡盛寿, 高润霖, 刘力生, 等. 《中国心血管病报告2018》概要. 中国循环杂志, 2019, 34(03): 209-220.
2. 李婷婷, 聂志超, 田甜, 等. 阿司匹林用于心血管疾病一级预防的Meta分析. 中国循证医学杂志, 2019, 19(10): 1187-1194.
3. 郑雯, 荣阳, 马殿忠, 等. 急性心肌梗死的早期治疗与临床研究. 中国医药指南, 2016, 14(16): 175-176.
4. 车千秋, 王琼英, 梁宇博, 等. 血管内超声与冠状动脉造影指导的经皮冠状动脉介入治疗左主干病变有效性和安全性的Meta分析. 中国循证医学杂志, 2020, 20(1): 71-78.
5. 颜红兵, 傅向华, 李春洁, 等. ST段抬高型急性心肌梗死院前溶栓治疗中国专家共识. 中国介入心脏病学杂志, 2018, 26(4): 181-190.
6. Stout K K, Daniels C J, Aboulhosn J A, et al. 2018 AHA/ACC guideline for the management of adults with congenital heart disease. J Am Coll Cardiol, 2019, 73(12): e81-e192.
7. Lundergan C F, Ross A M, Mccarthy W F, et al. Predictors of left ventricular function after acute myocardial infarction: Effects of time to treatment, patency, and body mass index. Am Heart J, 2001, 142(1): 43-50.
8. Liu W, Huang Q, Chang S, et al. Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed Signal Proces, 2018, 45: 22-32.
9. Reasat T, Shahnaz C. Detection of inferior myocardial infarction using shallow convolutional neural networks//2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). Dhaka: IEEE, 2017: 718-721.
10. 熊鹏, 薛彦平, 刘明, 等. 基于密集连接卷积神经网络的下壁心肌梗死检测. 生物医学工程学杂志, 2020, 37(1): 142-149.
11. Sharma L N, Tripathy R K, Dandapat S. Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Trans Biomed Eng, 2015, 62(7): 1827-1837.
12. Acharya U R, Fujita H, Sudarshan V K, et al. Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl-Based Syst, 2016, 99(1): 146-156.
13. Sharma L D, Sunkaria R K. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image Video Process, 2017, 12(2): 199-206.
14. Goldberger A L, Amaral L A N, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): e215-e220.
15. 刘秀玲, 乔磊, 杨建利, 等. 改进小波阈值法用于心电信号去噪. 生物医学工程学杂志, 2014, 31(3): 511-515.
16. Smith S. 急性心肌梗死心电图快速判读: 再灌注治疗循环手册. 天津: 天津科技翻译出版公司, 2004: 139-149.
17. 李洪奇. 急性心肌梗死心电图诊断分析. 世界最新医学信息文摘, 2017, 17(28): 146-148.
18. Pan J, Tompkins W J. A real-time QRS detection algorithm. IEEE Trans Biomed Eng, 1985, 32(3): 230-236.
19. 李露. 心电信号ST段分析研究. 重庆: 重庆大学, 2007.
20. 辛宇, 童孟军, 华宇婷. 一种基于最优特征选择改进的遗传算法. 传感技术学报, 2018, 31(11): 131-136.
21. 田江华, 卢广平, 张海澄, 等. 体表心电图在急性下壁心肌梗死中的定位诊断价值. 中国心血管杂志, 2018, 23(6): 477-480.
22. He K, Sun J. Convolutional neural networks at constrained time cost// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston: IEEE, 2015: 5353-5360.

Journal of Biomedical Engineering

Detection of inferior myocardial infarction based on morphological characteristics

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