Atrial fibrillation diagnosis algorithm based on improved convolutional neural network_Journal of Biomedical Engineering

Authors：

PU Yu ¹ ,  ZHU Junjiang ¹ , ZHANG Detao ² , YAN Tianhong ¹

1. College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310000, P.R.China;
2. SID Medical Co., Ltd., Shanghai 200030, P.R.China;

Corresponding author：

ZHU Junjiang, Email: zjj602@yeah.net

Keywords：

convolutional neural network; atrial fibrillation; loss function; false-negative rate

DOI：

10.7507/1001-5515.202007039

Video：

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Atrial fibrillation (AF) is a common arrhythmia, which can lead to thrombosis and increase the risk of a stroke or even death. In order to meet the need for a low false-negative rate (FNR) of the screening test in clinical application, a convolutional neural network with a low false-negative rate (LFNR-CNN) was proposed. Regularization coefficients were added to the cross-entropy loss function which could make the cost of positive and negative samples different, and the penalty for false negatives could be increased during network training. The inter-patient clinical database of 21 077 patients (CD-21077) collected from the large general hospital was used to verify the effectiveness of the proposed method. For the convolutional neural network (CNN) with the same structure, the improved loss function could reduce the FNR from 2.22% to 0.97% compared with the traditional cross-entropy loss function. The selected regularization coefficient could increase the sensitivity (SE) from 97.78% to 98.35%, and the accuracy (ACC) was 96.62%, which was an increase from 96.49%. The proposed algorithm can reduce the FNR without losing ACC, and reduce the possibility of missed diagnosis to avoid missing the best treatment period. Meanwhile, it provides a universal loss function for the clinical auxiliary diagnosis of other diseases.

Citation： PU Yu, ZHU Junjiang, ZHANG Detao, YAN Tianhong. Atrial fibrillation diagnosis algorithm based on improved convolutional neural network. Journal of Biomedical Engineering, 2021, 38(4): 686-694. doi: 10.7507/1001-5515.202007039 Copy

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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(3): 209-220.
2. Wang Z, Chen Z, Wang X, et al. The disease burden of atrial fibrillation in China from a national cross-sectional survey. Am J Cardiol, 2018, 122(5): 793-798.
3. 黄宛, 黄大显, 王思让, 等. 临床心电图学. 第五版. 北京: 人民卫生出版社, 2017: 356-357.
4. Wang T J, Larson M G, Levy D, et al. Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study. Circulation, 2003, 107(23): 2920-2925.
5. Hagiwara Y, Fujita H, Oh S L, et al. Computer-aided diagnosis of atrial fibrillation based on ECG signals: a review. Inform Sciences, 2018, 467: 99-114.
6. Kaufman E S, Waldo A L. The impact of asymptomatic atrial fibrillation. J Am Coll Cardiol, 2004, 43(1): 53-54.
7. Gladstone D J, Spring M, Dorian P, et al. Atrial fibrillation in patients with cryptogenic stroke. New Engl J Med, 2014, 370: 2467-2477.
8. Andreotti F, Carr O, Pimentel M A F, et al. Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG//2017 Computing in Cardiology (CinC). Rennes: IEEE, 2017: 1-4.
9. Xiong Z, Stiles M K, Zhao J. Robust ECG signal classification for detection of atrial fibrillation using a novel neural network//2017 Computing in Cardiology (CinC). Rennes: IEEE, 2017: 1-4.
10. Fan X, Yao Q, Cai Y, et al. Multiscaled fusion of deep convolutional neural networks for screening atrial fibrillation from single lead short ECG recordings. IEEE J Biomed Health Inform, 2018, 22(6): 1744-1753.
11. Andersen R S, Peimankar A, Puthusserypady S. A deep learning approach for real-time detection of atrial fibrillation. Expert Syst Appl, 2019, 115: 465-473.
12. Shi H, Wang H, Qin C, et al. An incremental learning system for atrial fibrillation detection based on transfer learning and active learning. Comput Methods Programs Biomed, 2020, 187: 105219.
13. Jin Y, Qin C, Huang Y, et al. Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks. Knowl-Based Syst, 2020: 105460.
14. Mousavi S, Afghah F, Acharya U R. HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks. arXiv preprint arXiv, 2020: 2002.05262.
15. 姚晰童, 代煜, 张建勋, 等. 陡脉冲干扰下的心电信号滤波及QRS提取. 工程科学学报, 2020, 42(5): 654-662.
16. 叶琳琳, 杨丹, 王旭. 基于集合经验分解与改进阈值函数的小波变换心电信号去噪方法研究. 生物医学工程学杂志, 2014, 31(3): 567-571.
17. Wang Z, Zhu J, Yan T, et al. A new modified wavelet-based ECG denoising. Comput Assist Surg, 2019, 24(sup1): 174-183.
18. 卢泓宇, 张敏, 刘奕群, 等. 卷积神经网络特征重要性分析及增强特征选择模型. 软件学报, 2017, 28(11): 2879-2890.
19. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436-444.
20. 周非, 李阳, 范馨月. 图像分类卷积神经网络的反馈损失计算方法改进. 小型微型计算机系统, 2019, 40(7): 1532-1537.
21. Nasr G E, Badr E A, Joun C. Cross entropy error function in neural networks: forecasting gasoline demand//The 15th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS). Florida: AAAI, 2002: 381-384.
22. Zubair M, Kim J, Yoon C. An automated ECG beat classification system using convolutional neural networks//2016 6th International Conference on IT Convergence and Security (ICITCS). Prague: IEEE, 2016: 1-5.
23. Erdenebayar U, Kim H, Park J U, et al. Automatic prediction of atrial fibrillation based on convolutional neural network using a short-term normal electrocardiogram signal. J Korean Med Sci, 2019, 34(7): e64.
24. Goodfellow I J, Vinyals O, Saxe A M. Qualitatively characterizing neural network optimization problems. arXiv preprint arXiv, 2014: 1412.6544.
25. Li Y, Tang X, Wang A, et al. Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation. Australasian Phys Eng Sci Med, 2017, 40(3): 707-716.
26. Faust O, Shenfield A, Kareem M, et al. Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Comput Biol Med, 2018, 102: 327-335.
27. Jin Y, Qin C, Liu J, et al. A novel domain adaptive residual network for automatic atrial fibrillation detection. Knowl-Based Syst, 2020: 106122.
28. Wang J. A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network. Future Generat Comput Syst, 2020, 102: 670-679.
29. Ammour N. Atrial fibrillation detection with a domain adaptation neural network approach//2018 International Conference on Computational Science and Computational Intelligence (CSCI). Las Vegas: IEEE, 2018: 738-743.
30. De Lannoy G, François D, Delbeke J, et al. Weighted conditional random fields for supervised interpatient heartbeat classification. IEEE Trans Biomed Eng, 2011, 59(1): 241-247.

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