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Abnormality Detection in Indian ECG using Correlation Techniques

by Shahanaz Ayub, J. P. Saini
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 14
Year of Publication: 2012
Authors: Shahanaz Ayub, J. P. Saini
10.5120/9352-3683

Shahanaz Ayub, J. P. Saini . Abnormality Detection in Indian ECG using Correlation Techniques. International Journal of Computer Applications. 58, 14 ( November 2012), 33-38. DOI=10.5120/9352-3683

@article{ 10.5120/9352-3683,
author = { Shahanaz Ayub, J. P. Saini },
title = { Abnormality Detection in Indian ECG using Correlation Techniques },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 14 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 33-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number14/9352-3683/ },
doi = { 10.5120/9352-3683 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:31.859745+05:30
%A Shahanaz Ayub
%A J. P. Saini
%T Abnormality Detection in Indian ECG using Correlation Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 14
%P 33-38
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper proposes a method based on signal processing correlation technique to find out whether the ECG is normal or abnormal. Many of the abnormal ECGs are called Arrhythmias. ECG (lead II) obtained from conventional ECG machine of Indian patients are digitized and the data are cross-correlated with the reference standard normal ECG data. Two different beats of the same ECG data are also correlated. The correlation parameters are used to identify the ECG as normal or abnormal. The accuracy obtained in this method is 100%. The cross-correlation is done using MATLAB 7. 12. 0 (R2011a) tools.

References
  1. Shahanaz Ayub, J. P. Saini, 'ECG classification and Abnormality Detection using Cascade Forward Neural Network" in International Journal of Engineering, Science & Technology, Vol. 3, No. 3, pp. 41-46, 2011.
  2. Shahanaz Ayub, J. P. Saini, 'Fusion Beats extraction from ECG using Neural Network based soft computing Techniques', published in International Journal of Advances in Applied Science Research', Coden (USA), 1(2), pp 76-83, 2010.
  3. Jalel Chebil, Jamal Al-Nabulsi, Mohammed Al-Maitah, "A Novel Method for Digitizing Standard ECG Papers", Proceedings of the International Conference on Computer and Communication Engineering, IEEE, pp. 1308-1312, 2008.
  4. Sucharita Mitra, M Mitra, "An Automated Data Extraction System from 12 Lead ECG Images", Computer Methods and Programs in Biomedicine, (Elsevier Science publication), vol. 71(1), pp 33-38, May 2003.
  5. MATLAB Simulink Help, The MathWorks, Inc. , MATLAB 7. 12. 0 (R2011a).
  6. The Radical Statistician: A Beginners Guide to Unleashing the Power of Applied Statistics in the Real World (5th Ed. ) Jim Higgins Publishing. 2006.
  7. Alias Bin Ramli and Putri Aidawati Ahmad "Correlation Analysis for Abnormal ECG Signal Features Extraction", 4th National Conference on Telecommunication Technology Proceedings, Shah Alam, Malaysia, IEEE Proceedings, pp. 232-237, 2003.
  8. Chuang-Chien Chiu, Tong-Hong Lin and Ben-Yi Liau "Using Correlation Coefficient in ECG Waveform for Arrhythmia Detection", Biomedical Engineering Applications, Basis & Communications, Vol. 17 No. 3, pp. 37-42, June 2005.
  9. Feng Zhao, Qingming Huang, Wen Gao, "Image Matching By Normalized Cross-Correlation", ICASSP, IEEE Proceedings, pp. 729-732, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Arrhythmia Cross-correlation ECG Lead II

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