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Development of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network - Part II: GUI Development

by Oludare Y. Ogundepo, Akinlolu A. Ponnle
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 3
Year of Publication: 2015
Authors: Oludare Y. Ogundepo, Akinlolu A. Ponnle
10.5120/ijais15-451379

Oludare Y. Ogundepo, Akinlolu A. Ponnle . Development of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network - Part II: GUI Development. International Journal of Applied Information Systems. 9, 3 ( June 2015), 26-36. DOI=10.5120/ijais15-451379

@article{ 10.5120/ijais15-451379,
author = { Oludare Y. Ogundepo, Akinlolu A. Ponnle },
title = { Development of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network - Part II: GUI Development },
journal = { International Journal of Applied Information Systems },
issue_date = { June 2015 },
volume = { 9 },
number = { 3 },
month = { June },
year = { 2015 },
issn = { 2249-0868 },
pages = { 26-36 },
numpages = {9},
url = { https://www.ijais.org/archives/volume9/number3/762-1379/ },
doi = { 10.5120/ijais15-451379 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:59:54.949954+05:30
%A Oludare Y. Ogundepo
%A Akinlolu A. Ponnle
%T Development of a Computer-Aided Application for Analyzing ECG Signals and Detection of Cardiac Arrhythmia Using Back Propagation Neural Network - Part II: GUI Development
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 9
%N 3
%P 26-36
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Electrocardiogram (ECG) is a graphic recording of the electrical activity produced by the heart. We have developed a computer-aided application model for classification of ECG signals for detection of cardiac arrhythmia. The model is based on some existing algorithms in literature which were adapted to suit our application. The developed model involves ECG signal pre-processing, extraction of some morphological features and simulating it with a trained Back Propagation Neural Network (BPNN) object. The application model has been investigated using the database of Massachusetts Institute of Technology Division of Health Science and Technology/Beth Israel Hospital (MIT-BIH). In this paper, in order to make the application (a software tool) user friendly, we present the development of a MATLAB based graphical user interface (GUI) for the application, which then makes it serve fully as a cost-effective computer aided application to analyze ECG signals for detection of cardiac arrhythmia. The performance of the developed application was investigated using the MIT-BIH database. Accuracy of 88. 6%, sensitivity of 90%, specificity of 86. 6%, and positive predictivity of 90% (based only on the MIT-BIH database used) shows that the developed application can be employed for the detection of heart diseases in patients. Also, the application is easy to use, fast, and gives the result of diagnosis as 'Normal' or 'Abnormal'.

References
  1. Upasani D. E and Kharadkar R. D. 2012, Automated ECG Diagnosis, IOSR Journal of Engineering, Vol. 2 (5), pp. 1265-1269.
  2. Ojha D. K. , and Subashini M. 2014, Analysis of Electrocardiograph (ECG) Signal for the Detection of Abnormalities Using MATLAB, International Journal of Medical, Health, Biomedical and Pharmaceutical Engineering, Vol. 8, No. 2, WASET, pp. 120-123.
  3. MATLAB R2015a Documentation, Peak Analysis, www. mathworks. com/help/signal/examples/peak-analysis. html
  4. Correia S. , Miranda J. , Silva L. and Barreto A. 2009, LabVIEW and MATLAB for ECG Acquisition, Filtering and Processing, In Proceedings of 3rd International Conference on Integrity, Reliability and Failure (IFR2009), Porto/Portugal, 20-24 July 2009, paper ref: S0228_A0402.
  5. Islam M. K. , Haque N. M. M. , Tangim G. , Ahammad T. , and Khondokar M. R. H. 2012, Study and Analysis of ECG Signal Using MATLAB and LABVIEW as Effective Tools, International Journal of Computer and Electrical Engineering, Vol. 4. , No. 3, pp. 404-408.
  6. Reaz M. B. I. , Amin M. S. , Husain H. , and Hashim F. H. 2011, An Economic Distant Fetal Electrocardiogram Monitoring System, Scientific Research and Essays, Vol. 6, No. 23, pp. 4975-4983.
  7. Vanage A. M. , Khade R. H. , and Shinde D. B. 2012, Classifying Five Different Arrhythmias by Analysing the ECG Signals, International Journal of Computational Engineering and Management, Vol. 15, Issue 4, pp. 75-80.
  8. Shaman P. 2014, ANN Based GUI for ECG Classification and Normality Detection, International Journal of Advances in Computer Science and Technology, Vol. 3, No. 6, pp. 383-385.
  9. Jaylaxmi C. M. and Raveendra M. 2014, MATLAB Based ECG Signal Classification, International Journal of Science, Engineering and Technology Research (IJSETR), Vol. 3, Issue 7, pp. 1946-1951.
  10. Mali B. , Zulj S. , Magjarevic R. , Miklavcic D. , and Jarm T. 2014, MATLAB-based Tool for ECG and HRV Analysis, Biomedical Signal Processing and Control, Elsevier, Vol. 10, pp. 108-116.
  11. Al-Ziarjawey H. A. H. and Cankaya I. 2015, Heart Rate Monitoring and PQRST Detection Based on Graphical User Interface with MATLAB, International Journal of Information and Electronics Engineering, Vol. 5, No. 4, pp. 311-316.
  12. Priya P. K. and Reddy G. U. 2015, MATLAB based GUI for Arrhythmia Detection Using Wavelet Transform, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 4, Issue 2, pp. 807-816.
  13. Bawa K. , and Sabharwal P. 2014, ECG Signal Fibrillation Classification on Android Platform: A Survey Approach, International Journal of Emerging Science and Engineering (IJESE), Vol. 2, Issue 7, pp. 1-4.
  14. Belgacem N. , Assous S. , and Bereksi-Reguig F. 2011, Bluetooth Portable Device and MATLAB-based GUI for ECG Signal Acquisition and Analysis, IEEE 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA), pp. 87-90.
  15. Oresko J. J. , Jin Z. , Cheng J. , Huang S. , Sun Y. , Duschl H. , and Cheng A. C. 2010, A Wearable Smartphone-Based Platform for Real Time Cardiovascular Disease Detection Via Electrocardiogram Processing, IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 3, pp. 734-740.
  16. Lin C-T. , Chang K-C. , Lin C-L. , Chiang C-C. , Lu S-W. , Chang S-S. , Lin B-S. , Liang H-Y. , Chen R-J. , Lee Y-T. , and Ko L-W. 2010, An Intelligent Telecardiology System Using a Wearable and Wireless ECG to Detect Atrial Fibrillation, IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 3, pp. 726-733.
  17. Hermawan K. , Iskandar A. A. , and Hartono R. N. 2011, Development of ECG Signal Interpretation Software on Android 2. 2, IEEE 2nd International Conference on Instrumentation, Communication, Information Technology and Biomedical Engineering (ICICI-BME), 8-9 Nov. , 2011, Bandung, Indonesia, pp. 259-264.
  18. The Mathworks, MATLAB Graphical User Interface (GUI), www. mathworks. com/discovery/matlab-gui. html
  19. The Mathworks, GUI Building Blocks, www. mathworks. com/help/matlab/gui-building-basics. html
  20. MIT-BIH Database distribution, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, 1998. http://www. physionet. org/physiobank/database/mitdb/
  21. Association for the Advancement of Medical Instrumentation, 1994, American National Standard for Ambulatory Electrocardiographs, Publication ANSI/AAMI EC38-1994.
  22. Pan J. and Tompkins W. J. 1985, A Real Time QRS Detection Algorithm, IEEE Transactions on Biomedical Engineering. BME-32(3), pp. 230-236.
  23. Hamilton P. S. and Tompkins W. J. 1986, Quantitative Investigation of QRS Detection Rules using the MIT/BIH Arrhythmia Database, IEEE Transactions on Biomedical Engineering. BME-33, pp. 1157-1165.
  24. Levenberg K. 1944, A Method for the Solution of Certain Problems in Least Squares, Quarterly of Applied Mathematics, Vol. 2, pp. 164-168.
  25. Marquardt D. 1963, An Algorithm for Least Squares Estimation of Non-linear Parameters, SIAM Journal on Applied Mathematics, Vol. 11(2), pp. 431-441.
  26. Hagan M. T. and Menhaj M. 1994, Training Feed-forward Networks with the Marquardt Algorithm, IEEE Transactions on Neural Networks, Vol. 5, No. 6, pp. 989-993.
  27. Chazal P. , Duyer M. O. , and Reilly R. B. 2004, Automatic Classification of Heartbeat using ECG Morphology and Heart Beat Interval Features, IEEE Transactions on Biomedical Engineering, Vol. 51, pp. 1196-1206.
  28. Owis M. I. , Youssef A. B. M. , and Kadah Y. M. 2002, Characteristics of Electrocardiogram Signals based on Blind Source Separation, IEEE Transactions on Medical and Biological Engineering and Computing, Vol. 40, Issue 5, pp. 557-564.
  29. Hadhoud M. M. A. , Eladawy M. I. , and Farag A. 2006, Computer Aided Diagnosis of Cardiac Arrhythmias, IEEE International Conference on Computer Engineering and Systems, 5-7 Nov. , 2006, Cairo, pp. 262-265.
  30. De Chazal P. and Reilly R. B. 2006, A Patient-Adapting Heartbeat Classifier using ECG Morphology and Heartbeat Interval Features, IEEE Transactions on Biomedical Engineering, Vol. 53, No. 12, pp. 2535-2543.
  31. Jen K. and Hwang Y. 2008, ECG Feature Extraction and Classification Using Cepstrum and Neural Networks, Journal of Medical and Biological Engineering, Vol. 28, No. 1, pp. 31-37.
  32. Ubeyli E. D. 2009, Statistics over Features of ECG Signals, Expert Systems with Applications, Vol. 36, No. 5, pp. 8758-8767.
  33. Das M. K. and Ari S. 2014, ECG Beats Classification Using Mixture of Features, International Scholarly Research Notices, Vol. 2014, Article ID: 178436, pp. 1-12.
  34. Velic M. , Padavic I. , and Car S. 2013, Computer Aided ECG Analysis - State of the Art and Upcoming Challenges, IEEE EUROCON 2013, 1-4 July, 2013, Zagreb, Croatia, pp. 1778-1784.
Index Terms

Computer Science
Information Sciences

Keywords

Electrocardiogram cardiac arrhythmia graphical user interface classification accuracy positive predictivity morphology.

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