ECG Signal Classification Based on Deep Learning

Abstract

Abstract: Electrocardiogram (ECG) can reflect the state of the heart in real time, and can be used for the accurate diagnosis of arrhythmias and other cardiovascular diseases. In view of the noise interference during ECG signal acquisition, we reconstruct the fourth-order components of Db6 wavelet, then use Butterworth low pass filter to realize double denoising. Then, from denoised ECG signals to extract the R-wave, and the P-QRS-T are intercepted and input into the one-dimensional improved GoogLeNet model for training. One-dimensional improved GoogLeNet is an improved structure of the original two-dimensional GoogLeNet, which reduces the network depth and adds the maximum pooled layer and dilated convolution in the sparse connection to increase the receptive field, so as to reduce the amount of calculation and improve the training performance. Experiments on the MIT-BIH data set show that the classification accuracy is 99.39%, which is 0.17 percentage points and 0.22 percentage points higher than the one-dimensional GoogLeNet and the original GoogLeNet respectively, and the training efficiency is improved. Signal classification has a marked improvement over other advanced techniques.

Key words: classification of arrhythmias, Db6 wavelet, low pass filtering, one-dimensional improved GoogLeNet, dilated convolution

YU Yan, QIU Lei, . ECG Signal Classification Based on Deep Learning[J]. Computer and Modernization, 2022, 0(05): 16-20.

References

［1］吴波. 基于心电信号的身份识别与心律失常识别方法研究［D］. 济南:山东大学, 2019.
［2］WANG T, LU C H, SUN Y N, et al. Automatic ECG classification using continuous wavelet transform and convolutional neural network［J］. Entropy, 2021,23(1). DOI: 10.3390/e23010119.
［3］马金伟,刘盛平. 心电信号识别分类算法综述［J］. 重庆理工大学学报(自然科学), 2018,32(12):122-128.
［4］侯晓晴,仝泽友,刘晓文. 基于改进小波变换的QRS特征提取算法研究［J］. 现代电子技术, 2020,43(13):57-61.
［5］王超超,彭勇,廖毅,等. ECG信号改进阈值函数小波去噪算法研究［J］. 电子技术与软件工程, 2020(1):74-75.
［6］XU B X. Application of ECG signal denoising and T-wave automatic detection based on computer deep learning［J］. Journal of Physics: Conference Series, 2020,1578(1). DOI: 10.1088/1742-6596/1578/1/012007.
［7］宋莉,孟庆建,张光玉,等. 基于波形特征和SVM的心电信号自动分类方法研究［J］. 中国医学物理学杂志, 2010,27(4):2043-2046.
［8］吴志勇,丁香乾,许晓伟,等. 基于深度学习和模糊C均值的心电信号分类方法［J］. 自动化学报, 2018,44(10):1913-1920.
［9］陶亮,刘宝宁,梁玮. 基于CNN-LSTM混合模型的心律失常自动检测［J］. 山东大学学报(工学版), 2021,51(3):30-36.
［10］叶思超,徐晨华,乔清理. 基于残差网络融合模型的心律失常分类研究［J］. 陕西师范大学学报(自然科学版), 2020,48(6):10-17.
［11］MOODY G B, MARK R G. The impact of the MIT-BIH arrhythmia database［J］. IEEE Engineering in Medicine and Biology Magazine, 2001,20(3):45-50.
［12］BLANCO-VELASCO M, WENG B W, BARNER K E. ECG signal denoising and baseline wander correction based on the empirical mode decomposition［J］. Computers in Biology and Medicine, 2008,38(1):1-13.
［13］卢莉蓉,王鉴,牛晓东. 基于VMD和小波阈值的ECG肌电干扰去噪处理［J］. 传感技术学报, 2020,33(6):867-873.
［14］LIN Y D, HU Y H. Power-line interference detection and suppression in ECG signal processing［J］. IEEE Transactions on Biomedical Engineering, 2008,55(1):354-357.
［15］AL-SALMAN W, LI Y, WEN P. Detecting sleep spindles in EEGs using wavelet fourier analysis and statistical features［J］. Biomedical Signal Processing and Control, 2019,48:80-92.
［16］KUMNGERN M, AUPITHAK N, KHATEB F, et al. 0.5 V fifth-order Butterworth low-pass filter using multiple-input OTA for ECG applications［J］. Sensors, 2020,20(24). DOI: 10.3390/s20247343.
［17］KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks［J］. Communications of the ACM, 2017,60(6):84-90.
［18］SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition［J］. arXiv preprint arXiv:1409.1556, 2014.
［19］ARORA S, BHASKARA A, GE R, et al. Provable bounds for learning some deep representations［C］// Proceedings of the 31st International Conference on Machine Learning. 2014,1:883-891.
［20］SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions［C］// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015:1-9.
［21］YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions［J］. arXiv preprint arXiv:1511.07122, 2015.
［22］李楠,蔡坚勇,李科,等. 基于多Inception结构的卷积神经网络人脸识别算法［J］. 计算机系统应用, 2020,29(2):157-162.
［23］MULVEY J M, RUSZCZYNSKI A. A new scenario decomposition method for large-scale stochastic optimization［J］. Operations Research, 1995,43(3):477-490.
［24］刘光达,周葛,董梦坤,等. 基于FFNN和1D-CNN的实时心律失常诊断系统与算法［J］. 电子测量与仪器学报, 2021,35(3):35-42.
［25］ZHAI X L, TIN C. Automated ECG classification using dual heartbeat coupling based on convolutional neural network［J］. IEEE Access, 2018,6:27465-27472.
［26］杨淑莹,桂彬彬,陈胜勇. 基于小波分解和1D-GoogLeNet的心律失常检测［J］. 电子与信息学报, 2021,43(10):3018-3027.
［27］李兴秀,唐建军,华晶. 结合CNN与双向LSTM的心律失常分类［J］. 计算机科学与探索, 2021,15(12):2353-2361.
［28］NEELA T, NAMBURU S. ECG signal classification using capsule neural networks［J］. IET Networks, 2021,10(3):103-109.

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