Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network

  • Authors

    • Dinesh D. Patil
    • R P. Singh
    • Vilas M. Thakare
    • Avinash K. Gulve
    2018-04-15
    https://doi.org/10.14419/ijet.v7i2.17.11553
  • Cuckoo Search, Cardiovascular disease, Electrocardiograms, Neural Network, QRS, Support Vector Machine.

  • Abstract

    This paper tried to address several topics concerning the analysis, synthesis and compression of the electrocardiogram signal (ECG) using the MIT database. We detect the R-wave by identifying the location of each interval delineating a QRS complex using unbiased and biased estimators. In the second part of the work, we segmented the signal into RR periods constituting the vectors of a data matrix, where we extracted its main components in order to reduce the size of the cardiac information, and then further reduced in addition the size by the use of a threshold on the signal. Then the classification is done for automatic detection of heart disease using Support Vector Machine (SVM) and Cuckoo Search Optimized Neural Network. ECG beats with 4 types of abnormalities (RBBB, APC, PVC and LBBB) from ECG records is retrieved from the MIT-BIH arrhythmia database. Analysis of the different groups shows the overall recognition performance was 99.50%. The worst is 99.63% for the RBBB class.

     

  • References

    1. [1] Ahlstrom, M.L. and Tompkins, W.J., 1985. Digital filters for real-time ECG signal processing using microprocessors. IEEE Transactions on Biomedical Engineering, (9), pp.708-713.

      [2] Luo, S. and Johnston, P., 2010. A review of electrocardiogram filtering. Journal of electrocardiology, 43(6), pp.486-496.

      [3] Okada, M., 1979. A digital filter for the ors complex detection. IEEE Transactions on Biomedical Engineering, (12), pp.700-703.

      [4] Islam, M.S. and Alajlan, N., 2013. A morphology alignment method for resampled heartbeat signals. Biomedical Signal Processing and Control, 8(3), pp.315-324.

      [5] Tamaki, S., Yamada, T., Okuyama, Y., Morita, T., Sanada, S., Tsukamoto, Y., Masuda, M., Okuda, K., Iwasaki, Y., Yasui, T. and Hori, M., 2009. Cardiac iodine-123 metaiodobenzylguanidine imaging predicts sudden cardiac death independently of left ventricular ejection fraction in patients with chronic heart failure and left ventricular systolic dysfunction: results from a comparative study with signal-averaged electrocardiogram, heart rate variability, and QT dispersion. Journal of the American College of Cardiology, 53(5), pp.426-435.

      [6] Olmos, S., García, J., Jané, R. and Laguna, P., 1999. Truncated orthogonal expansions of recurrent signals: equivalence to a linear time-variant periodic filter. IEEE transactions on signal processing, 47(11), pp.3164-3172.

      [7] Hosseini, H.G., Reynolds, K.J. and Powers, D., 2001. A multi-stage neural network classifier for ECG events. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE (Vol. 2, pp. 1672-1675). IEEE.

      [8] https://commons.wikimedia.org/wiki/File:Heart_diagram-en.svg

      [9] Poornachandra, S., 2008. Wavelet-based denoising using subband dependent threshold for ECG signals. Digital signal processing, 18(1), pp.49-55.

      [10] Thomas, M., Das, M.K. and Ari, S., 2015. Automatic ECG arrhythmia classification using dual tree complex wavelet based features. AEU-International Journal of Electronics and Communications, 69(4), pp.715-721.

      [11] Zidelmal, Z., Amirou, A., Adnane, M. and Belouchrani, A., 2012. QRS detection based on wavelet coefficients. Computer methods and programs in biomedicine, 107(3), pp.490-496.

      [12] Manab K. Das. Samit Ari., 2014. Patient-specific ECG beat classification technique. Healthcare Technology Letters. 1(3), pp.98–103.

      [13] R. Mark, G. Moody. MIT-BIH Arrhythmia database directory. Massachusette Inst. Technol. (MIT), 1988.

      [14] R. Mark, R. Wallen. AAMI-recommended practice: Testing and reporting performance results of ventricular arrhythmia detection algorithms. AAMI, Tech. Rep. ECAR, 1987.

      [15] Hosseini, H.G., Reynolds, K.J. and Powers, D., 2001. A multi-stage neural network classifier for ECG events. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE (Vol. 2, pp. 1672-1675). IEEE.

      [16] Acharya, U.R., Bhat, P.S., Iyengar, S.S., Rao, A. and Dua, S., 2003. Classification of heart rate data using artificial neural network and fuzzy equivalence relation. Pattern recognition, 36(1), pp.61-68.

      [17] Rai, H.M., Trivedi, A. and Shukla, S., 2013. ECG signal processing for abnormalities detection using multi-resolution wavelet transform and Artificial Neural Network classifier. Measurement, 46(9), pp.3238-3246.

      [18] Ronzhina, M., Janoušek, O., Kolářová, J., Nováková, M., Honzík, P. and Provazník, I., 2012. Sleep scoring using artificial neural networks. Sleep medicine reviews, 16(3), pp.251-263.

      [19] Yang, X.S. and Deb, S., 2009, December. Cuckoo search via Lévy flights. In Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on (pp. 210-214). IEEE.

      [20] Yang, X.S. and Deb, S., 2014. Cuckoo search: recent advances and applications. Neural Computing and Applications, 24(1), pp.169-174.

      [21] Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K. and Stanley, H.E., 2000. Physiobank, physiotoolkit, and physionet. Circulation, 101(23), pp.e215-e220.

      [22] Sowmya I, Ch. Sathi Raju, Md Zia-Ur-Rahman, D.V.R.K Reddy “Respiration Baseline wander removal from cardiac signals using an optimized Adaptive Noise cancellerâ€, Canadian Journal of Signal Processing, Vol-2, no-3, pp.27-31, 2011.

      [23] Md.Zia Ur Rahman, S.R.Ahamed and D.V.R.K Reddy, “Noise Cancellation in ECG Signals using Computationally Simplified Adaptive Filtering Techniques: Application to Biotelemetryâ€, Signal Processing: An International Journal, CSC Journals, ISSN 1985-2312, Vol. 3, Issue 5, pp. 1-12, 2009.

      [24] Md. Nizamuddin Salman, P. Trinatha Rao, Md.Zia Ur Rahman, “Cardiac Signal Enhancement Using Normalised Variable Step Algorithm For Remote Healthcare Monitoring Systems,†International Journal of Medical Engineering and Informatics, Inderscience Pub, Vol. 9, No. 2, 2017, pp. 145-161.

      [25] Md. Zia Ur Rahman, Adaptive Noise Cancellers for Cardiac Signal Enhancement for IOT Based Health Care Systems, Journal of Theoretical and Applied Information Technology,Vol.95, no.10, 2017, pp.2206-2213.

      [26] M. Nagesh, Md. Zia Ur Rahman, “A New ECG Signal Enhancement Strategy using Non-Negative Algorithmsâ€, International Journal of Control Theory and Applications Vol.10, no.35, 2017, pp.323-333.

      [27] G V S Karthik, Md. Zia Ur Rahman, “ECG Signal Enhancement using Circular Leaky Adaptive Algorithm in an IOT Enabled Sensor Systemâ€, International Journal of Control Theory and Applications,Vol.10, no.35, 2017, pp.271-282.

      [28] Md. Salman, Md. Zia Ur Rahman, “Efficient and Low Complexity Noise Cancellers for Cardiac Signal Enhancement using Proportionate Adaptive Algorithmsâ€, Indian Journal Science and Technology,Vol.9, no-37, pp. 1-11, October 2016.

      [29] M. Nagesh, Md. Zia Ur Rahman, “Efficient Noise Cancellers for ECG Signal Enhancement for Telecardiology Applicationsâ€, Leonardo Electronic Journal of Practices and Technologies,Issue 29, 2016, pp.79-92.

      [30] M. Nagesh, Md. Zia Ur Rahman, “Efficient Cardiac Signal Enhancement Techniques Based on Variable Step Size and Data Normalized Hybrid Signed Adaptive Algorithmsâ€, International Review on Computers and Software,Vol.11, no.10, 2016, pp.872-883.

      [31] B. Srikanth, Md. Zia Ur Rahman, “Efficient ECG Signal Conditioning Techniques using Variable Step Size LMF Algorithmsâ€, International Journal of Engineering and Technology,Vol. 8, No 2, pp.660-668, 2016.

      [32] Asiya Sulthana, Md. Zia Ur Rahman, “Design and Implementation of Efficient Low Complexity Biomedical Artifact Canceller for Nano Devicesâ€, Leonardo Electronic Journal of Practices and Technologies,Issue 28, pp. 197-210, 2016.

      [33] Md. Zia Ur Rahman, et. al., “Artifact Removal in ECG Signals using Modified Data Normalization Based Signal Enhancement Units for Health Care Monitoring Systemsâ€, Journal of Theoretical and Applied Information Technology,Vol.93, no.2, 2016, pp.540-551.

      [34] Sk. Nore Johny Basha, Md Zia-Ur-Rahman, and Dr.B.V. Rama Mohana Rao, “ Noise Removal from Electrocardiogram Signals using Leaky and Normalized version of Adaptive Noise Canceller,†International Journal of Computer Science & Communication Networks, ISSN: 2249-5789, Vol. 1(1), Sep.-Oct. 2011.

      [35] Shafi Shahsavar Mirza,Md Zia Ur Rahman, “Efficient Adaptive Filtering Techniques for Thoracic Electrical Bio-Impedance Analysis in Health Care Systemsâ€, Journal of Medical Imaging and Heath Informatics, Vol.7, no-9, pp. 1126-1138, 2017.

      [36] Md Zia Ur Rahman, Shafi Shahsavar Mirza,“Process Techniques For Human Thoracic Electrical Bio-Impedance Signal In Remote Healthcare Systems,†IET Healthcare Technology Letters, DOI: 10.1049/Htl.2015.0061, pp. 1–5, 2016.

      [37] G.V.S.Karthik, S. Y. Fathima, Md. Zia Ur Rahman, Sk.RafiAhamed , A. Lay-Ekuakille, “Reply to Comments on Efficient Signal conditioning techniques for Brain activity in Remote Health Monitoring Networkâ€, IEEE Sensors Journal, vol.15, no.9, pp.5351, 2015.

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  • How to Cite

    D. Patil, D., P. Singh, R., M. Thakare, V., & K. Gulve, A. (2018). Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network. International Journal of Engineering & Technology, 7(2.17), 27-33. https://doi.org/10.14419/ijet.v7i2.17.11553

    Received date: 2018-04-15

    Accepted date: 2018-04-15

    Published date: 2018-04-15