Elimination of Noise from Ambulatory ECG Signal using DWT

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Sonali Lohbare (Pakhmode) , Swati Dixit
DOI :  10.14445/22315381/IJETT-V70I5P229

Citation 

MLA Style: Sonali Lohbare (Pakhmode) and Swati Dixit. "Elimination of Noise from Ambulatory ECG Signal using DWT ." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 266-273. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P229

APA Style:Sonali Lohbare (Pakhmode) & Swati Dixit.(2022). Elimination of Noise from Ambulatory ECG Signal using DWT . International Journal of Engineering Trends and Technology, 70(5), 266-273. https://doi.org/10.14445/22315381/IJETT-V70I5P229

Abstract
The proposed work presents an efficient Wavelet Transform for noise removal from ambulatory cardiac signals and finds the R-R intervals making the signal ready for diagnosis. Noise affects cardiac analysis by creating some outliers. ECG signals can get corrupted by power line interference; direct current (DC) offset noise. These noises need to be filtered using moving average, wiener filter, etc. Some additional outlier bands might be attached to the ECG signal upon filtering. These signals can be removed using IIR (Infinite Impulse Response) and FIR (Finite Impulse Response) filters. Discrete Wavelet Transform (DWT) based technique designed using Python software is implemented in de-noising the Electrocardiograph (ECG) signal. The performance of de-noising is based on parameters like the selection of the wavelet (mother) function, the threshold method selected, the threshold value chosen, and the level of decomposition. Standard ECG signals available in the Physionet database, specifically the Arrhythmia Database MIT-BIH and PTB Database, are used to implement the proposed DWT method. Daubechies 8 (db8) is employed for high amplitude R wave detection. R waves are reference waves used in detecting the other waves by searching maxima and minima using windows. The same method also gives other features of R-R interval, QRS complex, P-R interval, ST deviation, and heart rate. SNR (Signal-to-Noise Ratio), MSE (Mean Square Error), RSME (Root Mean Square Error), and PSNR (Peak Signal-to-Noise Ratio) for soft and hard threshold techniques using Bayes Shrink and Visu Shrink methods were also estimated. The proposed approach gives maximum PSNR, average normalized MSE of 0.96, and SNR of 74%. The method proposed exhibits considerable noise elimination from ECG signals, and the de-noised signal obtained is suitable for ambulatory diagnosis.

Keywords
Discrete Wavelet Transform (DWT), cardiac signal, threshold method, SNR, MSE, R interval.

Reference
[1] David L. Donoho, De-Noising by Soft-Thresholding, in IEEE Transactions on Information Theory. 4(3) (1995) 613-627. Doi:10.1109/18.382009.
[2] Shubhojeet Chatterjee, et al., Review of Noise Removal Techniques in ECG Signals, IET Journals Publication. (2020). https://dio.org/10.1049/iet-spr.2020.0104
[3] Jezewski Michal, Kahankova Radana, et al., A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography, IEEE Reviews in Biomedical Engineering. 13 (2020) 51-73.
[4] Gordan Cornelia, Reiz Romulus, ECG Signals Processing using Wavelets, University of Oradea: Electronics Department, Oradea, Romania.
[5] Manjin Liu, Mei Hui, Ming Liu, Liquan Dong, Zhu Zhao, and Yuejin Zhao, ECG Signals De-Noising Using Wavelet Transform and Independent Component Analysis, Proceedings, SPIE. 9622 (2015) 962213. https://doi.org/10.1117/12.2193108
[6] Weidong Zhou, Jean Gotman, Removal of EMG and ECG Artifacts from EEG Based on Wavelet Transform and ICA, Proceeding of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, USA. (2004) 392-395. Doi:10.1109/IEMBS.2004.1403176.
[7] H.Y. Lin, S.Y. Liang, et al., Discrete-Wavelet-Transform-Based Noise Removal and Feature Extraction for ECG Signals, IRBM. 35 (2014) 351-361. http://dx.doi.org/10.1016/j.irbm.2014.10.004
[8] Patil Harishchandra, Holambe R., New Approach of Threshold Estimation for De-Noising ECG Signal Using Wavelet Transform, Annual IEEE India Conference (INDICON). (2013). Doi:10.1109/INDCON 2013.6726038
[9] Zhe Li, Jun Ni, Xin Gu, A De-Noising Framework for ECG Signal Processing, sixth IEEE International Conference on Internet Computing for Science and Engineering. (2012). Doi: 10.1109/ICICSE.2012.59
[10] Paul S Addison, Wavelet Transforms and the ECG: A Review, Physiological Measurement. (2005) R155-R199. Doi:10.1088/0967- 3334/26/5/R01
[11] V. Afonso, W. Tompkins, T. Nquyen, S. Luo, ECG Beat Detection Using Filter Banks, IEEE Trans. Biomed. Eng. 46(2) (1999) 192– 201.
[12] C. Li, C. Zheng, C. Tai, Detection of ECG Characteristic Points by Wavelet Transform, IEEE Trans. Biomed. Eng. 42(1) (1995) 21–28.
[13] R.J. Brychta, S. Tuntrakool, el. at., A. Diedrich, Wavelet Methods for Spike Detection in Mouse Renal Sympathetic Nerve Activity, IEEE Trans. Biomed. Eng. 54(1) (2007) 82–93.
[14] T. Sharma, K.K. Sharma, QRS Complex Detection in ECG Signals Using Locally Adaptive Weighted Total Variation Denoising, Comput. Biol. Med. 87 (2017) 187–199.
[15] Sonali P. Lohbare, Swati Dixit, and Shubhada Deshpande, End-to-End Supporting System for IoT Applications: Survey, ICCCE, Lecture Notes in Electrical Engineering. 828 (2021) 931- 939. https://doi.org/10.1007/978-981-16-7985-8_99
[16] (2021). The MIT-BIH Arrhythmia Database 1.0.0 [Online] Available: Error! The hyperlink reference is not valid.
[17] Ingrid Daubechies, The Wavelet Transform, Time-Frequency Localization, and Signal Analysis, IEEE Transactions on Information Theory. 36(5) (1990). Doi: 0018-9448/90/0900-0961
[18] Mohammed Azmi AI-Betar, ?-Hill Climbing: An Exploratory Local Search, Neural Computing and Applications. 28. Doi: 10.1007/s00521-016-2328-2
[19] K. Asaduzzaman, M.B.I. Reaz, F. Mohd-Yasin, K.S.Sim, M. S. Hussain, A Study on Discrete Wavelet-Based Noise Removal from EEG Signals, Advance in Computational Biology. 680 (2010) 593-599. Doi: 10.1007/978-1-4419-5913-3_65
[20] David L. Donoho, Iain M. Johnstone, Ideal Spatial Adaptation by Wavelet Shrinkage, Biometrika. 81(3) (1994) 425-455. https://doi.org/10.2307/2337118
[21] Levent Sendur, Ivan W. Selesnick, A Bio-Variate Shrinkage Function for Wavelet Based De-noising, , IEEE International Conference on Acoustics, Speech, and Signal and Signal Processing – Proceedings. 2 (2002) 11/1261-11/1264. https://doi.org/10.1109/icassp.2002.5744031
[22] Raaed Faleh Hassan et al., ECG Signal De-Noising and Feature Extraction using Discrete Wavelet Transform, International Journal of Engineering Trends and Technology (IJETT). 63(1) (2018).