ECG signal diagnoses using intelligent systems

  • Authors

    • Raaed Faleh Hassan Department of Medical Instrument Engineering Techniques, Electrical Engineering Technical College, Middle Technical University
    • Sally Abdulmunem Shaker Department of Medical Instrument Engineering Techniques, Electrical Engineering Technical College, Middle Technical University
    2018-09-26
    https://doi.org/10.14419/ijet.v7i4.16485
  • ECG, Backpropagation, Fuzzy, MATLAB, Cardiovascular Diseases

  • Abstract

    Accurate diagnosis of arrhythmias plays a crucial role in saving the lives of many heart patients. The aim of this research is to find the more efficient method to diagnosis electrocardiogram (ECG) diseases. This work presents the use of Backpropagation neural network (BPNN) and fuzzy logic for automatic detection of cardiac arrhythmias based on analysis of the ECG. These a more valuable tool used to classify ECG signals in cardiac patients. Data collected from physioBank ATM. The analysis of the ECG signal is performed in MATLAB environment. In BPNN the results appear that the only two misclassifications happened to result in an accuracy of 90.4%. while in fuzzy inference system the results appear that the classification accuracy is 100%.

     

     

     

  • References

    1. [1] A. A. M. A. A. B. Zahia Zidelmala, "QRS detection based on wavelet coefficients," Elsevier, 2011.

      [2] B. Michael W. Zimmerman, Classification of ECG St Events as Ischemic or Non-Ischemic Using Reconstructed Phase Spaces, A Thesis submitted to the Faculty of the Graduate School, Marquette University, and May 2004.

      [3] M. K. El Mimouni El Hassan *, "An FPGA-Based Implementation of a Pre-Processing Stage for ECG Signal Analysis Using DWT," in Second World Conference on Complex Systems, Agadir, Morocco, 2014 IEEE.

      [4] B. N. L. M. C. D. Wai Kei Lei, "AFC-ECG: An Adaptive Fuzzy ECG Classifier," in Springer, Verlag Berlin Heidelberg, 2007.

      [5] M. A. M. Taiseer Mohammed Siddig, "A Study of ECG Signal Classification using Fuzzy Logic Control," International Journal of Science and Research, vol. 3, no. 2, 2014.

      [6] G. K.Amtul Salam, "An Algorithm for ECG Analysis of Arrhythmia Detection," in International Conference on Electrical, Computer and Communication Technologies, Coimbatore, India, 2015 IEEE.

      [7] Y.-S. N. a. J.-Y. W. Kun-Chih (Jimmy) Chen*, "Electrocardiogram Diagnosis using Wavelet-based Artificial," in IEEE 5th Global Conference on Consumer Electronics, 2016.

      [8] R. M.-M. D. L.-E. D. C. T.-A. E. R.-A. M.-P. R. C.-M. Jose Antonio Gutiérrez-Gnocchi, "DSP-based arrhythmia classification using wavelet transform and probabilistic neural network," Elsevier, no. 1746-8094, 2016.

      [9] A. S. B. R. G. M. D. Sayali Tandale, "Arrhythmia Classification Using Neuro-Fuzzy Approach," IEEE, 2017.

      [10] R. P. JAAKKO MAMIVUO, "Principles and Applications of Bioelectric and Biomagnetic Fields," in the Basis of ECG Diagnosis, 1995.

      [11] "https://www.physionet.org/cgi-bin/atm/ATM," [Online].

      [12] A. T. Hari Mohan Rai, "ECG signal classification using wavelet transform and Back Propagation Neural Network," in 5th International Conference on Computers and Devices for Communication, Kolkata, India, 2012 IEEE.

      [13] M. M. H. T. B. Tanoy Debnath, "Analysis of ECG Signal and Classification of Heart Abnormalities Using Artificial Neural Network," in 9th International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh, 2016 IEEE.

      [14] D. N. S. S. N. D. G. Abishek Santhosh Raj A, "Auto Analysis of ECG Signals Using Artificial Neural Network," in International Conference on Science, Engineering and Management Research, Chennai, India, 2014 IEEE.

      [15] D. K. P. I. A. B. K. K. Remya R S, "Classification of Myocardial Infarction Using Multi-Resolution Wavelet Analysis of ECG," in International Conference on Emerging Trends in Engineering, Science and Technology, 2015 Elsevier.

      [16] V. K. G. Mayank Kumar Gautam, "A Neural Network approach and Wavelet analysis for ECG classification," in second IEEE International Conference on Engineering and Technology, Coimbatore, TN, India. 2016 IEEE.

      M. S. Uvais Qidwai, "Fuzzy Detection of Critical Cardiac Abnormalities using ECG data: A ubiquitous approach," in 11th International Conference on Hybrid Intelligent Systems, Melacca, Malaysia, 2011 IEEE

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  • How to Cite

    Faleh Hassan, R., & Abdulmunem Shaker, S. (2018). ECG signal diagnoses using intelligent systems. International Journal of Engineering & Technology, 7(4), 2733-2737. https://doi.org/10.14419/ijet.v7i4.16485

    Received date: 2018-07-30

    Accepted date: 2018-09-04

    Published date: 2018-09-26