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

Sucharita Mitra Sarkar; Priyanka Samanta

doi:doi:10.11648/j.jeee.20231104.12

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

Sucharita Mitra Sarkar, Priyanka Samanta

Published in Journal of Electrical and Electronic Engineering (Volume 11, Issue 4)

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

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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.

Published in	Journal of Electrical and Electronic Engineering (Volume 11, Issue 4)
DOI	10.11648/j.jeee.20231104.12
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), 2023. Published by Science Publishing Group

Keywords

De-noising, Wavelet, Decomposition, Threshold, Reconstruction

References

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[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).

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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

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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

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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

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@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}
}

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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  -

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Author Information

Sucharita Mitra Sarkar

Department of Electronics, Netaji Nagar Day College, Kolkata, India
Priyanka Samanta

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

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Plain Text BibTeX RIS

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

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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