CFP last date
22 April 2024
Reseach Article

An Approach for ECG Feature Extraction using Daubechies 4 (DB4) Wavelet

by Muhidin A. Mohamed, Mohamed A. Deriche
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 96 - Number 12
Year of Publication: 2014
Authors: Muhidin A. Mohamed, Mohamed A. Deriche
10.5120/16850-6712

Muhidin A. Mohamed, Mohamed A. Deriche . An Approach for ECG Feature Extraction using Daubechies 4 (DB4) Wavelet. International Journal of Computer Applications. 96, 12 ( June 2014), 36-41. DOI=10.5120/16850-6712

@article{ 10.5120/16850-6712,
author = { Muhidin A. Mohamed, Mohamed A. Deriche },
title = { An Approach for ECG Feature Extraction using Daubechies 4 (DB4) Wavelet },
journal = { International Journal of Computer Applications },
issue_date = { June 2014 },
volume = { 96 },
number = { 12 },
month = { June },
year = { 2014 },
issn = { 0975-8887 },
pages = { 36-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume96/number12/16850-6712/ },
doi = { 10.5120/16850-6712 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:21:37.183937+05:30
%A Muhidin A. Mohamed
%A Mohamed A. Deriche
%T An Approach for ECG Feature Extraction using Daubechies 4 (DB4) Wavelet
%J International Journal of Computer Applications
%@ 0975-8887
%V 96
%N 12
%P 36-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An Electrocardiogram (ECG) signal describes the electrical activity of the heart recorded by electrodes placed on the surface of human body. It summarizes an important electrical activity used for the primary diagnosis of heart abnormalities such as Tachycardia, Bradycardia, Normalcy, Regularity and Heart Rate Variation. The most clinically useful information of the ECG signal is found in the time intervals between its consecutive waves and amplitudes defined by its features. In this paper, an ECG feature extraction algorithm based on Daubechies Wavelet Transform is presented. DB4 Wavelet is selected due to the similarity of its scaling function to the shape of the ECG signal. R peaks detection is the core of this algorithm's feature extraction. All other primary peaks are extracted with respect to the location of R peaks through creating windows proportional to their normal intervals. The proposed extraction algorithm is evaluated on MIT-BIH Arrhythmia Database. Experimental results indicate that the algorithm can successfully detect and extract all the primary features with a deviation error of less than 10%.

References
  1. K. Huang and L. Zhang, "Cardiology knowledge free ECG feature extraction using generalized tensor rank one discriminant analysis," EURASIP Journal on Advances in Signal Processing, vol. 2014, pp. 1-15, 2014.
  2. J. L. Garvey, "ECG techniques and technologies," Emergency medicine clinics of North America, vol. 24, pp. 209-225, 2006.
  3. S. Mahmoodabadi, A. Ahmadian, and M. Abolhasani, "ECG feature extraction using Daubechies wavelets," in Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, 2005, pp. 343-348.
  4. Q. Zhao and L. Zhang, "ECG feature extraction and classification using wavelet transform and support vector machines," in Neural Networks and Brain, 2005. ICNN&B'05. International Conference on, 2005, pp. 1089-1092.
  5. E. B. Mazomenos, D. Biswas, A. Acharyya, T. Chen, K. Maharatna, J. Rosengarten, J. Morgan, and N. Curzen, "A low-complexity ECG feature extraction algorithm for mobile healthcare applications," IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, vol. 17, p. 459, 2013.
  6. B. Salsekar and A. Wadhwani, "FILTERING OF ECG SIGNAL USING BUTTERWORTH FILTER AND ITS FEATURE EXTRACTION," International Journal of Engineering Science & Technology, vol. 4, 2012.
  7. F. M. Vaneghi, M. Oladazimi, F. Shiman, A. Kordi, M. Safari, and F. Ibrahim, "A comparative approach to ECG feature extraction methods," in Proceedings of 3rd international conference on intelligent systems modeling and simulation, 2012, pp. 08-10.
  8. A. Espiritu-Santo-Rincon and C. Carbajal-Fernandez, "ECG feature extraction via waveform segmentation," in Electrical Engineering Computing Science and Automatic Control (CCE), 2010 7th International Conference on, 2010, pp. 250-255.
  9. S. Karpagachelvi, M. Arthanari, and M. Sivakumar, "ECG feature extraction techniques-a survey approach," arXiv preprint arXiv:1005. 0957, 2010.
  10. J. Saraswathy, M. Hariharan, V. Vijean, S. Yaacob, and W. Khairunizam, "Performance comparison of Daubechies wavelet family in infant cry classification," in Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on, 2012, pp. 451-455.
  11. G. B. Moody and R. G. Mark, "The impact of the MIT-BIH arrhythmia database," Engineering in Medicine and Biology Magazine, IEEE, vol. 20, pp. 45-50, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

ECG feature extraction Daubechies Wavelets cardiac signal