CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

ECG Signal Denoising and Ischemic Event Feature Extraction using Daubechies Wavelets

by H. S. Niranjana Murthy, M. Meenakshi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 2
Year of Publication: 2013
Authors: H. S. Niranjana Murthy, M. Meenakshi
10.5120/11369-6630

H. S. Niranjana Murthy, M. Meenakshi . ECG Signal Denoising and Ischemic Event Feature Extraction using Daubechies Wavelets. International Journal of Computer Applications. 67, 2 ( April 2013), 29-33. DOI=10.5120/11369-6630

@article{ 10.5120/11369-6630,
author = { H. S. Niranjana Murthy, M. Meenakshi },
title = { ECG Signal Denoising and Ischemic Event Feature Extraction using Daubechies Wavelets },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 2 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number2/11369-6630/ },
doi = { 10.5120/11369-6630 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:23:38.127093+05:30
%A H. S. Niranjana Murthy
%A M. Meenakshi
%T ECG Signal Denoising and Ischemic Event Feature Extraction using Daubechies Wavelets
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 2
%P 29-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The ability of an intelligent system to correctly classify and recognize patterns makes them particularly suitable for use in an expert system that aids in the interpretation of ECG signals. The features extracted from ECG are highly useful in diagnosis. Wavelet based methods present a best performance as irregularity measures and makes them suitable for ECG data analysis. In this paper, we propose an algorithm for detection of myocardial Ischemic episodes from Electrocardiogram (ECG) signal using Daubechies Wavelet transform technique. The ECG signal was denoised by removing the corresponding wavelet coefficients at higher scale. Analysis is carried out using MATLAB software. The algorithm was evaluated using two cases of data, the first case is with healthy subjects, and second case is with subjects affected by myocardial Ischemia. ECGs are obtained from MIT-BIH Arrhythmia Database which is manually annotated and developed for validation. From the results, it is concluded that Daubechies wavelets are best suitable for small datasets and are able to clearly demark the healthy and disease subjects such as myocardial ischemia subjects.

References
  1. Features Extraction of ECG signal for Detection of Cardiac arrhythmias by P. D. Khandait, N. G. Bawane, S. S. Limaye: National Conference on Innovative paradigms in Engineering & Technology (NCIPET-2012), proceedings published by International Journal of Computer Applications (IJCA)
  2. Ebrahimzadeh, A. , A. Khazaee, and V. Ranaee. Classification of the electrocardiogram signals using supervised classifiers and efficient features. Comput. Methods Programs Biomed. 99:179–194, 2010.
  3. Acharya, U. R. , M. Sankaranarayanan, J. Nayak, C. Xiang, and T. Tamura. Automatic identification of cardiac health using modeling techniques: a comparative study. Inform. Sci. 178:4571–4582, 2008.
  4. Alan Jovic, and Nikola Bogunovic, "Feature Extraction for ECG Time- Series Mining based on Chaos Theory," Proceedings of 29th International Conference on Information Technology Interfaces, 2007.
  5. V. S. Chouhan, and S. S. Mehta, "Detection of QRS Complexes in 12- lead ECG using Adaptive Quantized Threshold," IJCSNS International Journal of Computer Science and Network Security, vol. 8, no. 1, 2008.
  6. Yu, S. N. , and K. T. Chou. Selection of significant for ECG beat classification. Expert Syst. Appl. 36:2088–2096, 2009.
  7. Kuo-Kuang Jen, and Yean-Ren Hwang, "ECG Feature Extraction and Classification Using Cepstrum and Neural Networks," Journal of Medical and Biological Engineering, vol. 28, no. 1, 2008.
  8. Ebrahimzadeh, A. , A. Khazaee, and V. Ranaee. Classification of the electrocardiogram signals using supervised classifiers and efficient features. Comput. Methods Programs Biomed. 99:179–194, 2010.
  9. Khadra, L. , A. S. Al-Fahoum, and S. Binajjaj. A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques. IEEE Trans. Biomed. Eng. 52:1840–1845, 2005.
  10. N. V. Thakor, J. G. Webster and W. J. Tompkins, "Estimation of QRS complex power spectra for design of a QRS filter", IEEE Transactions on Biomedical Engineering, vol. BME-31, no. 11,pp. 702-706, 1986, pp. 702-706, Nov,1986
  11. E. D. Ubeyli, (2008) "Implementing wavelet transform / mixture of experts network for analysis of ECG beats", Expert system Vol. 25,pp. 150-162.
  12. Robert J. Huzar, Basic Dysrhythmias, Interpretation and Management (C. V. Mosby Co. , 1988).
  13. Goldberg AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit and PhysioNet: Components of a New Research Resource for Complex Physological Sirnals. Circulation 101(23):e215-e220
Index Terms

Computer Science
Information Sciences

Keywords

Daubechies wavelets Myocardial Ischemia Feature extraction

Powered by PhDFocusTM