Journal of Electrical and Electronic Engineering

| Peer-Reviewed |

Performance Evaluation of a Modified ECG De-noising Technique Using Wavelet Decomposition and Threshold Method

Received: 2 July 2023    Accepted: 9 August 2023    Published: 22 August 2023
Views:       Downloads:

Share This Article

Abstract

According to recent survey due to drastically changing weather and unhealthy lifestyle, irrespective of age people are suffer from different health issues, among them heart related diseases are very common. So to prevent some emergency health hazards due to such kind of diseases distant and continuous health monitoring is very useful, but due to lack of expert intervention both processes are very sensitive to noise. So our aim is to get a noise free medical data through above said processes to treat a patient properly. In this work experimental signal data is chosen from a 12 lead noisy ECG database which is formed using a MATLAB coded program by taking noisy and clear data from MIT-BIH noise stress test database and CSE clear ECG database respectively. Generated noisy ECG signals are decomposed using wavelet decomposition. Distorted coefficients generated during the process are recovered using threshold technique and the de-noised signal is achieved using changed coefficients. After de-noising process amplitude and duration of different segments and intervals of de-noised ECG signals for several SNR values and also for clear ECG signals are obtained by running an ECG feature extraction program developed in MATLAB. Compare both parameters to study the performance of the whole de-noising procedure, Again sensitivity, predictivity and detection accuracy are checked for each de-noised data for different SNR values and represent them graphically to detect the accuracy of the process.

DOI 10.11648/j.jeee.20231104.12
Published in Journal of Electrical and Electronic Engineering (Volume 11, Issue 4, August 2023)
Page(s) 89-98
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

De-noising, Wavelet, Decomposition, Threshold, Reconstruction

References
[1] Dengyong Zhang, Shanshan Wang, Feng Li, Jin Wang, Arun Kumar Sangaiah, Victor S. Sheng, Xiangling Ding, An ECG Signal De-noising approach based on wavelet energy and sub-band smoothing filter, Appl. Sci.(2019).
[2] Bhumika Chandrakar, O. P. Yadav, V. K. Chandra, A SURVEY OF NOISE REMOVAL TECHNIQUES FOR ECG SIGNALS, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 3, March (2013).
[3] Aung Soe Khaing, Zaw Min Naing, Quantitative Investigation of Digital Filters in Electrocardiogram with Simulated Noises, International Journal of Information and Electronics Engineering, Vol. 1, No. 3, November (2011).
[4] B. Halder, S. Mitra, M. Mitra, Detection and Identification of ECG waves by Histogram approach, (2016).
[5] G D Clifford, J Behar, Q Li and I Rezek, Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms, Physiol. Meas. Vol. 33, 1419–1433, (2012).
[6] Moody G B, Muldrow W E and Mark R G, A noise stress test for arrhythmia detectors Comput. Cardiol. 11: 381–4, (1984).
[7] Goldberger A L, Amaral L A N, Glass L, Hausdorff J M, Ivanov P C, Mark R G, Mietus J E, Moody G B, Peng C-Kand Stanley H E, PhysioBank, Physiotoolkit, and PhysioNet: components of a new research resource for complex physiologic signals Circ. 101 e215–20 PMID: 1085218, (2000).
[8] Dower, G. E., Machado, H. B., and Osborne, J. A., On deriving the electrocardiogram from vectorcardiographic leads. ClinCardiol, 3 (2), 87–95, (1980).
[9] StephaneMeystre, The Current State Of Telemonitoring: A Comment On TheLiterature, Telemedicine and e-health, Vol-11, November 1, (2005).
[10] SucharitaMitra, M. Mitra, and B. B. Chaudhuri, A Rough Set Based Approach for ECG Classification, Transactions on Rough Sets vol. IX, LNCS 5390, pp. 157–186, (2008).
[11] S. K. Mukhopadhyay, M. Mitra, S. Mitra, “ECG Feature Extraction Using Differentiation, Hilbert Transform, Variable Threshold and Slope Reversal Approach”, Journal of Medical Engineering and Technology vol. 36, no. 7, pp. 358-365, (2012).
[12] Sucharita Mitra, M. Mitra & B. B. Chaudhuri, A Rough-Set-Based Inference for ECG Classification, IEEETtransactionson Instrumentation and Measurement, vol. 55, no. 6, pp. 2198–2206, (2006).
[13] Sucharita Mitra, M. Mitra & B. B. Chaudhuri, “Pattern Defined Heuristic Rules and Directional Histogram Based Online ECG Parameter Extraction”, Measurement (Elsevier), vol. 42, pp. 150-156, (2009).
[14] V. Saritha, Sukanya, and Y. Narasimha Murthy, ‘ECG Signal Analysis Using Wavelet Transforms’, Bulgarian Journal of Physics, vol. 35, pp. 68-77, (2008).
[15] Mallat, S. A Wavelet Tour of Signal Processing; Academic Press: San Diego, CA, USA, (1998).
[16] SucharitaMitra and PriyankaSamanta, “Evaluation of different parameters of noisy ECG signal generated by jointly using MIT-BIH Noise Stress Test Database and CSE multi-lead clear ECG database”, JETIR, Volume 6, Issue 5, May (2019).
Cite This Article
  • APA Style

    Sucharita Mitra Sarkar, Priyanka Samanta. (2023). Performance Evaluation of a Modified ECG De-noising Technique Using Wavelet Decomposition and Threshold Method. Journal of Electrical and Electronic Engineering, 11(4), 89-98. https://doi.org/10.11648/j.jeee.20231104.12

    Copy | Download

    ACS Style

    Sucharita Mitra Sarkar; Priyanka Samanta. Performance Evaluation of a Modified ECG De-noising Technique Using Wavelet Decomposition and Threshold Method. J. Electr. Electron. Eng. 2023, 11(4), 89-98. doi: 10.11648/j.jeee.20231104.12

    Copy | Download

    AMA Style

    Sucharita Mitra Sarkar, Priyanka Samanta. Performance Evaluation of a Modified ECG De-noising Technique Using Wavelet Decomposition and Threshold Method. J Electr Electron Eng. 2023;11(4):89-98. doi: 10.11648/j.jeee.20231104.12

    Copy | Download

  • @article{10.11648/j.jeee.20231104.12,
      author = {Sucharita Mitra Sarkar and Priyanka Samanta},
      title = {Performance Evaluation of a Modified ECG De-noising Technique Using Wavelet Decomposition and Threshold Method},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {11},
      number = {4},
      pages = {89-98},
      doi = {10.11648/j.jeee.20231104.12},
      url = {https://doi.org/10.11648/j.jeee.20231104.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20231104.12},
      abstract = {According to recent survey due to drastically changing weather and unhealthy lifestyle, irrespective of age people are suffer from different health issues, among them heart related diseases are very common. So to prevent some emergency health hazards due to such kind of diseases distant and continuous health monitoring is very useful, but due to lack of expert intervention both processes are very sensitive to noise. So our aim is to get a noise free medical data through above said processes to treat a patient properly. In this work experimental signal data is chosen from a 12 lead noisy ECG database which is formed using a MATLAB coded program by taking noisy and clear data from MIT-BIH noise stress test database and CSE clear ECG database respectively. Generated noisy ECG signals are decomposed using wavelet decomposition. Distorted coefficients generated during the process are recovered using threshold technique and the de-noised signal is achieved using changed coefficients. After de-noising process amplitude and duration of different segments and intervals of de-noised ECG signals for several SNR values and also for clear ECG signals are obtained by running an ECG feature extraction program developed in MATLAB. Compare both parameters to study the performance of the whole de-noising procedure, Again sensitivity, predictivity and detection accuracy are checked for each de-noised data for different SNR values and represent them graphically to detect the accuracy of the process.},
     year = {2023}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Performance Evaluation of a Modified ECG De-noising Technique Using Wavelet Decomposition and Threshold Method
    AU  - Sucharita Mitra Sarkar
    AU  - Priyanka Samanta
    Y1  - 2023/08/22
    PY  - 2023
    N1  - https://doi.org/10.11648/j.jeee.20231104.12
    DO  - 10.11648/j.jeee.20231104.12
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 89
    EP  - 98
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20231104.12
    AB  - According to recent survey due to drastically changing weather and unhealthy lifestyle, irrespective of age people are suffer from different health issues, among them heart related diseases are very common. So to prevent some emergency health hazards due to such kind of diseases distant and continuous health monitoring is very useful, but due to lack of expert intervention both processes are very sensitive to noise. So our aim is to get a noise free medical data through above said processes to treat a patient properly. In this work experimental signal data is chosen from a 12 lead noisy ECG database which is formed using a MATLAB coded program by taking noisy and clear data from MIT-BIH noise stress test database and CSE clear ECG database respectively. Generated noisy ECG signals are decomposed using wavelet decomposition. Distorted coefficients generated during the process are recovered using threshold technique and the de-noised signal is achieved using changed coefficients. After de-noising process amplitude and duration of different segments and intervals of de-noised ECG signals for several SNR values and also for clear ECG signals are obtained by running an ECG feature extraction program developed in MATLAB. Compare both parameters to study the performance of the whole de-noising procedure, Again sensitivity, predictivity and detection accuracy are checked for each de-noised data for different SNR values and represent them graphically to detect the accuracy of the process.
    VL  - 11
    IS  - 4
    ER  - 

    Copy | Download

Author Information
  • Department of Electronics, Netaji Nagar Day College, Kolkata, India

  • Department of Electronics, Vidyasagar College for Women, Kolkata, India

  • Sections