Detection of inferior myocardial infarction based on densely connected convolutional neural network_Journal of Biomedical Engineering

Authors：

XIONG Peng , XUE Yanping , LIU Ming , DU Haiman , 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：

LIU Xiuling, Email: liuxiuling121@hotmail.com

Keywords：

inferior myocardial infarction; electrocardiogram; densely connected convolutional neural network; feature extraction

DOI：

10.7507/1001-5515.201904028

Video：

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Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as arrhythmia, heart failure and cardiogenic shock. Therefore, it is of great clinical value to carry out accurate and efficient early diagnosis of inferior myocardial infarction. Electrocardiogram is the most sensitive means for early diagnosis of inferior myocardial infarction. This paper proposes a method for detecting inferior myocardial infarction based on densely connected convolutional neural network. The method uses the original electrocardiogram (ECG) signals of serially connected Ⅱ, Ⅲ and aVF leads as the input of the model and extracts the robust features of the ECG signals by using the scale invariance of the convolutional layers. The characteristic transmission of ECG signals is enhanced by the dense connectivity between different layers, so that the network can automatically learn the effective features with strong robustness and high recognition, so as to achieve accurate detection of inferior myocardial infarction. The Physikalisch Technische Bundesanstalt diagnosis public ECG database was used for verification. The accuracy, sensitivity and specificity of the model reached 99.95%, 100% and 99.90%, respectively. The accuracy, sensitivity and specificity of the model are also over 99% even though the noise exists. Based on the results of this study, it is expected that the method can be introduced in the clinical environment to help doctors quickly diagnose inferior myocardial infarction in the future.

Citation： XIONG Peng, XUE Yanping, LIU Ming, DU Haiman, WANG Hongrui, LIU Xiuling. Detection of inferior myocardial infarction based on densely connected convolutional neural network. Journal of Biomedical Engineering, 2020, 37(1): 142-149. doi: 10.7507/1001-5515.201904028 Copy

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1. 陈伟伟, 高润霖, 刘力生, 等. 《中国心血管病报告2017》概要. 中国循环杂志, 2018, 33(1): 1-8.
2. De Luca G, Suryapranata H, Ottervanger J P, et al. Time delay to treatment and mortality in primary angioplasty for acute myocardial infarction: every minute of delay counts. Circulation, 2004, 109(10): 1223-1225.
3. 颜红兵, 向定成, 刘红梅, 等. ST段抬高型急性心肌梗死院前溶栓治疗中国专家共识. 中国实用内科杂志, 2018, 38(5): 434-442.
4. 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.
5. 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. Knowledge-Based Systems, 2016, 99: 146-156.
6. Arif M, Malagore I A, Afsar F A. Detection and localization of myocardial infarction using K-nearest neighbor classifier. J Med Syst, 2012, 36(1): 279-289.
7. Liu Wenhan, Huang Qijun, Chang Sheng, et al. Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram. Biomed Signal Process Control, 2018, 45: 22-32.
8. Sharma L D, Sunkaria R K. Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach. Signal Image and Video Processing, 2018, 12(2): 199-206.
9. Reasat T, Shahnaz C. Detection of inferior myocardial infarction using shallow convolutional neural networks, 2017 IEEE Region 10 Humanitarian Technology Conference. DOI: 10.1109/R10-HTC.2017.8289058.
10. Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
11. 李玉明, 李振有. 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.
12. 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): 148.
13. 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, 30(3): 511-515.
14. Pan J, Tompkins W J. A real-time QRS detection algorithm, IEEE Transactions on Biomedical Engineering, 1985, 32(3): 230–236.
15. Huang Gao, Liu Zhuang, van der Maaten L, et al. Densely connected convolutional networks//30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Hawaii, 2017: 2261-2269.
16. Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift//Proceedings of the 32th International Conference on Machine Learning.Lille, France, 2015: 448-456.
17. Nair V, Hinton G E. Rectified linear units improve restricted Boltzmann machines//Proceedings of the 27th International Conference on Machine Learning.Haifa, Israel, 2010: 807-814.
18. Zhang Zhicheng, Liang Xiaokun, Dong Xu, et al. A sparse-view CT reconstruction method based on combination of densenet and deconvolution. IEEE Transactions on Medical Imaging, 2018, 37(6): 1407-1417.
19. 梁蒙蒙, 周涛, 张飞飞, 等. 卷积神经网络及其在医学图像分析中的应用研究. 生物医学工程学杂志, 2018, 35(6): 977-985.
20. 张杰烁, 刘明, 李鑫, 等. 基于递归最小二乘法的回声状态网络算法用于心电信号降噪. 生物医学工程学杂志, 2018, 35(4): 539-549.

Journal of Biomedical Engineering

Detection of inferior myocardial infarction based on densely connected convolutional neural network

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