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Field Programmable Gate Array Based Fuzzy Neural Signal Processing System for Differential Diagnosis of QRS Complex Tachycardia and Tachyarrhythmia in Noisy ECG Signals

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Abstract

The paper reports of a Field Programmable Gate Array (FPGA) based embedded system for detection of QRS complex in a noisy electrocardiogram (ECG) signal and thereafter differential diagnosis of tachycardia and tachyarrhythmia. The QRS complex has been detected after application of entropy measure of fuzziness to build a detection function of ECG signal, which has been previously filtered to remove power line interference and base line wander. Using the detected QRS complexes, differential diagnosis of tachycardia and tachyarrhythmia has been performed. The entire algorithm has been realized in hardware on an FPGA. Using the standard CSE ECG database, the algorithm performed highly effectively. The performance of the algorithm in respect of QRS detection with sensitivity (Se) of 99.74% and accuracy of 99.5% is achieved when tested using single channel ECG with entropy criteria. The performance of the QRS detection system has been compared and found to be better than most of the QRS detection systems available in literature. Using the system, 200 patients have been diagnosed with an accuracy of 98.5%.

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References

  1. Engelse, W. A. H., and Zeelenberg, C., A single scan algorithm for QRS detection and feature extraction. Comp. Cardiol, IEEE Computer Society. 37–42, 1979.

  2. Okada, M., A digital filter for the QRS complex detection. IEEE Trans. Biomed. Eng. BME-26(12):700–703, 1979.

    Article  Google Scholar 

  3. Benitez, D., Gaydecki, P. A., Zaidi, A., and Fitzpatrick, A. P., The use of Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31:399–406, 2001.

    Article  Google Scholar 

  4. Chen, S. W., Chen, H. C., and Chan, H. L., A real time QRS detection method based on moving-averaging incorporating with wavelet denoising. Comput. Meth. Programs Biomed. 82:187–195, 2006.

    Article  Google Scholar 

  5. Kyrkos, A., Giakoumakis, E. A., and Carayannis, G., QRS detection through time recursive prediction technique. Signal Process. 15:429–436, 1988.

    Article  Google Scholar 

  6. Mehta, S. S., and Lingayat, N. S., Development of SVM based classification techniques for the delineation of wave components in 12-lead electrocardiogram. Biomed. Signal Process. Control 3(4):341–349, 2008.

    Article  Google Scholar 

  7. Murthy, I. S. N., and Prasad, G. S. S. D., Analysis ECG from pole zero models. IEEE Trans. Biomed. Eng. BME-39(7):741–751, 1992.

    Article  Google Scholar 

  8. Fraden, J., and Neurman, M. R., QRS wave detection. Med. Biol. Eng. Comp. 18:125–132, 1980.

    Article  Google Scholar 

  9. Pan, J., and Tompkins, W. J., A real time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32:230–236, 1985.

    Article  Google Scholar 

  10. Mehta, S. S., and Lingayat, N. S., ECG pattern classification using support vector machine 12-lead combined entropy. The Sixth International Conference on Advances in Pattern recognition, Indian Statistical Institute: Kolkata, India, 2007.

  11. Murthy, I. S. N., and Prasad, G. S. S. D., Analysis of ECG from pole zero models. IEEE Trans. Biomed. Eng. BME-39(7):741–751, 1992.

    Article  Google Scholar 

  12. Kohler, B. U., Henning, C., and Orglmeister, R., The principles of software QRS detection. IEEE Eng Med Biol. 42–47, 2002.

  13. Gritzali, F., Towards a generalized scheme for QRS detection in ECG waveforms. Signal Process. 15:183–192, 1988.

    Article  Google Scholar 

  14. Benitez, D., Gaydecki, P. A., Zaidi, A., and Fitzpatrick, A. P., The use of Hilbert transform in ECG signal analysis. Comput. Biol. Med. 31:399–406, 2001.

    Article  Google Scholar 

  15. Chen, S. W., Chen, H. C., and Chan, H. L., A real time QRS detection method based on moving-averaging incorporating with wavelet denoising. Computer Methods and Programs. In Biomedicine. 82:187–195, 2006.

    Google Scholar 

  16. Christov, I., Gómez-Herrero, G., Krasteva, V., Jekova, I., Gotchev, A., and Egiazarian, K., Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification. Med. Eng. Phys. 28(9):876–887, 2006.

    Article  Google Scholar 

  17. Knopfmacher, J., On measures of fuzziness. J. Math. Anal. Appl. 49:529–534, 1975.

    Article  MathSciNet  MATH  Google Scholar 

  18. Pal, N. R., and Bezdek, J. C., Measuring fuzzy uncertainty. IEEE Trans. Fuzzy Syst. 2(2):107–118, 1994.

    Article  Google Scholar 

  19. Czogala, E., and Gottwald, S., Measures of fuzziness: a survey. In: Singh, M. G., (Ed.), Systems & control encyclopaedia. Pergamon Press, 2963–2965, 2000.

  20. De Luca, A., and Termini, S., On the convergence of entropy measures of a fuzzy set. Kybernetes 6(3):219–227, 1977.

    Article  MATH  Google Scholar 

  21. Hayakawa, Y., and Nakajima, K., Characterization of inverse delayed model for neural computation. In Proceedings of International Symposium on non-linear theory and its applications, 861–864, 2002.

  22. Futane, N. P., Roy Chowdhury, S., Roychoudhuri, C., and Saha, H., CMOS analog ASIC design of inverse delayed function model of a neuron for ANN. IEEE VLSI Design and Test Symposium, Bangalore, July 8–10, 2009.

  23. Wellens, H. J., Ventricular tachycardiac diagnosis of broad QRS complex tachycardia. Heart. 86:579–585, 2001.

    Article  Google Scholar 

  24. Van Alste, J. A., and Schilder, T. S., Removal of base-line wander and power line interference from the ECG by an efficient FIR filter with a reduced number of taps. IEEE Trans. Biomed. Eng. 32:1052–1059, 1985.

    Article  Google Scholar 

  25. Furno, G. S., and Tompkins, W. J., A learning filter for removing noise interference. IEEE Trans. Biomed. Eng. 30:234–235, 1983.

    Article  Google Scholar 

  26. Kukar, M., Transductive reliability estimation in medical diagnosis. Artif. Intell. Med. 29:81–106, 2003.

    Article  Google Scholar 

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Acknowledgements

The author acknowledges the support lent by Ministry of Communications and Information Technology for providing the necessary fund to successfully carry out the research work. Thanks are also due to Prof. Hiranmay Saha of the Department of Electronics and Telecommunications Engineering, Jadavpur University for providing necessary help and support to develop the FPGA board.

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  1. IC Design and Fabrication Centre, Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, 700032, India

    Shubhajit Roy Chowdhury

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  1. Shubhajit Roy Chowdhury
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Correspondence to Shubhajit Roy Chowdhury.

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Chowdhury, S.R. Field Programmable Gate Array Based Fuzzy Neural Signal Processing System for Differential Diagnosis of QRS Complex Tachycardia and Tachyarrhythmia in Noisy ECG Signals. J Med Syst 36, 765–775 (2012). https://doi.org/10.1007/s10916-010-9543-7

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  • DOI: https://doi.org/10.1007/s10916-010-9543-7

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