Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (5): 179-186.doi: 10.11896/jsjkx.240100009

• Computer Graphics & Multimedia • Previous Articles     Next Articles

ECG Signal Denoising Method Based on Stationary Wavelet Transform with Hyperbolic TangentThreshold Function

WANG Haiyong1,2,3, DING Gufei1   

  1. 1 College of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    2 National Key Laboratory of Autonomous Marine Vehicle Technology Laboratory,Harbin Engineering University,Harbin 150001,China
    3 Smart Campus Research Centre,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Received:2024-01-02 Revised:2024-06-24 Online:2025-05-15 Published:2025-05-12
  • About author:WANG Haiyong,born in 1979,Ph.D,senior engineer.His main research interests include network security and computer vision.
  • Supported by:
    National Natural Science Foundation of China(61872190),Jiangsu Planned Projects for Postdoctoral Research Funds(2020Z058) and Stable Supporting Fund of National Key Laboratory of Autonomous Marine Vehicle Technology(2024-HYHXQ-WDZC06).

PDF (PC)

56

Abstract: In the acquisition process of ECG signals,there are various kinds of noise filled in the ECG signals,which will make the ECG signals become difficult to identify,thus affecting the diagnosis of medical personnel.Denoising the ECG signal is an important part of ECG signal research.This paper adopts the technique based on stationary wavelet transform,aiming at the defects of hard threshold and soft threshold in the denoising process of stationary wavalet transform,a hyperbolic tangent function withvariable parameters(SWTaVHT) is proposed for denoising ECG signals.Moreover,in order to prevent the loss of some high frequency information segments in the process of denoising,the R-peak location information assisted correction method is used to better retain useful signal features.In order to evaluate the effectiveness of SWTaVHT,experiments are conducted on the public database MIT-BIH for a comparative study with existing methods.Experimental results show that the signal-to-noise ratio(SNR),root-mean-square error(RMSE) and percentage root-mean-square difference(PRD) after denoising are better compared to the existing methods.The SWTaVHT denoises the ECG data without changing the amplitude of the original signals,which is better than the existing methods.

Key words: Electrocardiogram(ECG), Threshold function, Stationary wavelet transform, R-peaks correction, Denoising

CLC Number: 

  • TP391.9
[1]CHATTERJEE S,THAKUR R S,YADAV R N,et al.Review of noise removal techniques in ECG signals[J].IET Signal Processing,2020,14(9):569-590.
[2]BHASKAR P,UPLANE M.High frequency electromyogramnoise removal from electrocardiogram using FIR low pass filter based on FPGA[J].Procedia Technology,2016,25:497-504.
[3]KHER R.Signal processing techniques for removing noise from ECG signals[J].Journal of Biomedical Engineering and Researc,2019,3(101):1-9.
[4]SINGHAL A,SINGH P,FATIMAH B,et al.An efficient re-moval of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique[J].Biomedical Signal Processing and Control,2020,57:101741.
[5]KRISHNAN S R,SEELAMANTULA C S.On the selection of optimum Savitzky-Golay filters[J].IEEE Transactions on Signal Processing,2012,61(2):380-391.
[6]MOEIN S.An MLP neural network for ECG noise removalbased on Kalman filter[C]//Advances in Computational Biology.2010:109-116.
[7]RAKSHIT M,DAS S.An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter[J].Biomedical Signal Processing and Control,2018,40:140-148.
[8]KUMAR A,KOMARAGIRI R,KUMAR M.Design of wavelet transform based electrocardiogram monitoring system[J].ISA Transactions,2018,80:381-398.
[9]DEEPAK B,VANDANA C R,SOURYA D,et al.Motion Artifact Removal in Ambulatory ECG Signal for Heart Rate Variability Analysis[J].IEEE Sensors Journal,2019,19(24):12432-12442.
[10]MADAN P,SINGH V,SINGH D P,et al.Denoising of ECGsignals using weighted stationary wavelet total variation[J].Biomedical Signal Processing and Control,2022,73:103478.
[11]KUMAR A,TOMAR H,MEHLA V K,et al.Stationary wavelet transform based ECG signal denoising method[J].ISA Tran-sactions,2021,114:251-262.
[12]MERAH M,ABDELMALIK T,LARBI B.R-peaks detectionbased on stationary wavelet transform[J].Computer Methods and Programs in Biomedicine,2015,121(3):149-160.
[13]STEPHANE M.A wavelet tour of signal processing[M].Elsevier,1999.
[14]BAYER F M,KOZAKEVICIUS A J,CINTRA R J.An iterative wavelet threshold for signal denoising[J].Signal Processing,2019,162:10-20.
[15]GAO L,GAN Y,SHI J.A novel intelligent denoising method of ecg signals based on wavelet adaptive threshold and mathematical morphology[J].Applied Intelligence,2022,52(9):10270-10284.
[16]SAVITZKY A,GOLAY M J.Smoothing and differentiation of data by simplified least squares procedures[J].Analytical Chemis-try,1964,36(8):1627-1639.
[17]PAN J,TOMPKINS W J.A real-time QRS detection algorithm[J].IEEE Transactions on Biomedical Engineering,1985(3):230-236.
[18]NAYAK C,SAHA S K,KAR R,et al.Automated QRS complex detection using MFO－based DFOD[J].IET Signal Proces-sing,2018,12(9):1172-1184.
[19]BODA S,MAHADEVAPPA M,DUTTA P K.A hybrid method for removal of power line interference and baseline wander in ECG signals using EMD and EWT[J].Biomedical Signal Processing and Control,2021,67:102466.
[20]LI C,WU Y,LIN H,et al.ECG denoising method based on an improved VMD algorithm[J].IEEE Sensors Journal,2022,22(23):22725-22733.
[21]NAYAK J,SWAPNAREKHA H,NAIK B,et al.25 years of particle swarm optimization:Flourishing voyage of two decades[J].Archives of Computational Methods in Engineering,2023,30(3):1663-1725.
[22]KUO R,LI S S.Applying particle swarm optimization algo-rithm-based collaborative filtering recommender system considering rating and review[J].Applied Soft Computing,2023,135:110038.
[23]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.
[1] YUAN Zhen, LIU Jinfeng. Denoising Autoencoders Based on Lossy Compress Coding [J]. Computer Science, 2024, 51(6A): 230400172-7.
[2] ZHANG Jie, LU Miaoxin, LI Jiakang, XU Dayong, HUANG Wenxiao, SHI Xiaoping. Residual Dense Convolutional Autoencoder for High Noise Image Denoising [J]. Computer Science, 2024, 51(6A): 230400073-7.
[3] XUE Jinqiang, WU Qin. Progressive Multi-stage Image Denoising Algorithm Combining Convolutional Neural Network and
Multi-layer Perceptron [J]. Computer Science, 2024, 51(4): 243-253.
[4] SI Weina, YE Jun, JIANG Bin. Hyperspectral Image Denoising Combining Group Sparse and Representative Coefficient Bidirectional Spatial Spectral Total Variation [J]. Computer Science, 2024, 51(12): 199-208.
[5] YAN Zhihao, ZHOU Zhangbing, LI Xiaocui. Survey on Generative Diffusion Model [J]. Computer Science, 2024, 51(1): 273-283.
[6] HOU Yanrong, LIU Ruixia, SHU Minglei, CHEN Changfang, SHAN Ke. Review of Research on Denoising Algorithms of ECG Signal [J]. Computer Science, 2023, 50(6A): 220300094-11.
[7] ZHANG Lihong, YE Jun. Hyperspectral Image Denoising Based on Group Sparse and Constraint Smooth Rank Approximation [J]. Computer Science, 2023, 50(6): 209-215.
[8] YIN Haitao, WANG Tianyou. Image Denoising Algorithm Based on Deep Multi-scale Convolution Sparse Coding [J]. Computer Science, 2023, 50(4): 133-140.
[9] LI Yueyue, LIU Wanping, HUANG Dong. Image Denoising Network Model Combined with Multi-head Attention Mechanism [J]. Computer Science, 2023, 50(11A): 230100091-8.
[10] WEI Kai-xuan, FU Ying. Re-parameterized Multi-scale Fusion Network for Efficient Extreme Low-light Raw Denoising [J]. Computer Science, 2022, 49(8): 120-126.
[11] ZHANG Xin, SUN Jing, YANG Hong-bo, PAN Jia-hua, GUO Tao, WANG Wei-lian. Heart Sound Segmentation Algorithm Based on TK Energy Operator and Envelope Fusion [J]. Computer Science, 2022, 49(11A): 210900135-6.
[12] WU Yong, LIU Yong-jian, TANG Tang, WANG Hong-lin, ZHENG Jian-cheng. Hyperspectral Image Denoising Based on Robust Low Rank Tensor Restoration [J]. Computer Science, 2021, 48(11A): 303-307.
[13] CHEN Jin-yin, CHENG Kai-hui and ZHENG Hai-bin. Deep Learning Based Modulation Recognition Method in Low SNR [J]. Computer Science, 2020, 47(6A): 283-288.
[14] SUN Zhi-qiang, WAN Liang, DING Hong-wei. Android Malware Detection Method Based on Deep Autoencoder Network [J]. Computer Science, 2020, 47(4): 298-304.
[15] LUO Yue-tong,BIAN Jing-shuai,ZHANG Meng,RAO Yong-ming,YAN Feng. Detection Method of Chip Surface Weak Defect Based on Convolution Denoising Auto-encoders [J]. Computer Science, 2020, 47(2): 118-125.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.