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A statistical designing approach to MATLAB based functions for the ECG signal preprocessing

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Abstract

Though a number of research articles are available based on different preprocessing methodologies, all of them are not available in a single article with the practical utility of implementation. This article scopes to implement the most effective ECG signal preprocessing methods (baseline wander removal, noise cancellation, and peaks detection) with a simple statistical explanation. In addition, this research work contributes to designing several MATLAB based functions to implement the aforementioned preprocessing steps in practice. These functions are available free of cost for all in the MATLAB archive. The foremost aim of this article was to present clear conception about different ECG signal preprocessing steps like baseline wandering removal, noise elimination, QRS complex and point detections, P-peak and T-peak detection, and beat rate calculation. We hope that this work will eradicate all constraints of the ECG signal preprocessing and will motivate the new researchers of this field to reach more acute findings from the ECG signals.

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References

  1. Becker, D.E.: Fundamentals of electrocardiography interpretation. Anesth. Prog. 53(2), 53–64 (2006)

    Article  Google Scholar 

  2. Rivera-Ruiz, M., Cajavilca, C., Varon, J.: Einthoven’s string galvanometer: the first electrocardiograph. Tex. Heart Inst. J. 35(2), 174–178 (1927)

    Google Scholar 

  3. Beyramienanlou, H., Lotfivand, N.: Shannon’s energy based algorithm in ECG signal processing. Comput. Math. Methods Med. 2017, 1–16 (2017)

    Article  Google Scholar 

  4. Mejhoudi, S., Latif, R., Toumanari, A., Jenkal, W., Elouardi, A.: Implementation and evaluation of ECG signal processing algorithms on embedded architectures. In: 2017 International Conference on Electrical and Information Technologies (ICEIT), Rabat, Morocco, 2017, pp. 1–6 (2017)

  5. Qureshi, R., Uzair, M., Khurshid, K.: Multistage adaptive filter for ECG signal processing. In: 2017 International Conference on Communication, Computing and Digital Systems (C-CODE), Islamabad, Pakistan, 2017, pp. 363–368 (2017)

  6. Shin, S.W., Kim, K.S., Song, C.G., Lee, J.W., Kim, J.H., Jeung, G.W.: Removal of baseline wandering in ECG signal by improved detrending method. BioMed. Mater. Eng. 26, S1087–S1093 (2015)

    Google Scholar 

  7. Agrawal, S., Gupta, A.: Fractal and EMD based removal of baseline wander and powerline interference from ECG signals. Comput. Biol. Med. 43, 1889–1899 (2015)

    Article  Google Scholar 

  8. Manivel, K., Rabindran, R.S.: Noise removal for baseline wander and power line in electrocardiograph signals. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 4, 1114–1122 (2015)

    Google Scholar 

  9. Mali, B., Zulj, S., Magjarevic, R., Miklavcic, D., Jarm, T.: Matlab-based tool for ECG and HRV analysis. Biomed. Signal Process. Control 10, 108–116 (2014)

    Article  Google Scholar 

  10. Kohler, B.U., Hennig, C., Orglmeister, R.: The principles of software QRS detection. In: IEEE Engineering in Medicine and Biology Magazine, vol. 21, no. 1, pp. 42–57, Jan.–Feb. 2002 (2002)

  11. Diker, A., Avci, E., Gedıkpinar, M.: Determination of R-peaks in ECG signal using hilbert transform and pan-tompkins algorithms. In: 25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, 2017, pp. 1–4 (2017)

  12. Elgendi, M., Mohamed, A., Ward, R.: Efficient ECG compression and QRS detection for e-health applications. Sci. Rep. 7, 1–16 (2017)

    Article  Google Scholar 

  13. Chen, C., Chuang, C.: A QRS detection and R point recognition method for wearable single-lead ECG devices. Sensors 17(9), 1–19 (2017)

    Article  Google Scholar 

  14. Qin, Q., Jianqing, l., Yinggao, Y., Chengyu, L.: An adaptive and time-efficient ECG R-peak detection algorithm. J. Healthc. Eng. 2017, 1–14 (2017)

  15. Rahman, M.A.: MATLAB based functions for ECG signal preprocessing. MATLAB Central File Exchange. Retrieved October 16, 2018. https://www.mathworks.com/matlabcentral/fileexchange/69118-matlab-based-functions-for-ecg-signal-preprocessing. Accessed 16 Oct 2018

  16. Liu, F., Liu, C., Jiang, X., et al.: Performance analysis of ten common QRS detectors on different ECG application cases. J. Healthc. Eng. 2018, 1–8 (2018)

    Google Scholar 

  17. Queiroz, J.A., Barros, A.K.: Computer diagnosis for arrhythmia and atrial fibrillation based on electrocardiogram voltage variation. J. Cardiol. Clin. Res. 6(2), 1–4 (2018)

    Google Scholar 

  18. Lyon, A., Mincholé, A., Martínez, J.P., Laguna, P., Rodriguez, B.: Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J. R. Soc. Interface 15, 1–18 (2018)

    Article  Google Scholar 

  19. Mandala, S., Di, T.C.: ECG parameters for malignant ventricular arrhythmias: a comprehensive review. J. Med. Biol. Eng. 37(4), 441–453 (2017)

    Article  Google Scholar 

  20. Meijborg, V.M.F., Conrath, C.E., Opthof, T., Belterman, C.N.W., de Bakker, J.M.T., Coronel, R.: Electrocardiographic T wave and its relation with ventricular repolarization along major anatomical axes. Circ. Arrhythm. Electrophysiol. 7, 524–531 (2014)

    Article  Google Scholar 

  21. Ideker, R.E., Kong, W., Pogwizd, S.: Purkinje fibers and arrhythmias. Pacing Clin. Electrophysiol. 32(3), 283–285 (2009)

    Article  Google Scholar 

  22. Rahman, M.A., Milu, M.M.H., Anjum, A., Khanam, F., Ahmad, M.: Baseline wandering removal from ECG signal by wandering path finding algorithm. In: 3rd International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh, 2017, pp. 1–5 (2017). https://doi.org/10.1109/eict.2017.8275164

  23. Gilani, S.O., Ilyas, Y., Jamil, M.: Power line noise removal from ECG signal using notch, band stop and adaptive filters. In: International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI, Hawaii, 2018, pp. 1–4 (2018)

  24. Huamani, R., Talavera, J.R., Mendoza, E.A.S., Dávila, N.M., Supo, E.: Implementation of a real-time 60 Hz interference cancellation algorithm for ECG signals based on ARM cortex M4 and ADS1298. In: International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Peru, 2017, pp. 1–4 (2017)

  25. Rana, K.P.S., Kumar, V., Gupta, A.: A pole-radius-varying IIR notch filter with enhanced post-transient performance. Biomed. Signal Process. Control 33, 379–391 (2017)

    Article  Google Scholar 

  26. Khanam, F., Rahman, M.A., Ahmad, M.: Evaluating alpha relative power of EEG signal during psychophysiological activities in Salat. In: International Conference on Innovations in Science, Engineering and Technology 2018 (ICISET), 27–28 October, 2018, International Islamic University Chittagong (IIUC), Bangladesh, pp. 1–6 (2018)

  27. Zhang, F., Fu, J.: Noise elimination based on moving average by Guassian distribution weighting method. In: 2nd International Conference on Control, Automation and Robotics (ICCAR), Hong Kong, 2016, pp. 169–172 (2016)

  28. Awal, M.A., Mostafa, S.S., Ahmad, M.: Performance analysis of Savitzky–Golay smoothing filter using ECG signal. Int. J. Comput. Inf. Technol. 1(2), 24–29 (2011)

    Google Scholar 

  29. Mahamdya, M.A., Riley, H.B.: Performance study of different denoising methods for ECG signals. In: 4th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, ICTH-2014, vol. 34, pp. 325–332 (2014). https://doi.org/10.1016/j.procs.2014.08.048

  30. Rahman, M.A., Haque, M.M., Anjum, A., Khanam, F., Mollah, M.N., Ahmad, M.: Classification of motor imagery events from prefrontal hemodynamics for BCI application. In: International Joint Conference on Computational Intelligence (IJCCI), 14–15 December, 2018, Daffodil International University, Dhaka, Bangladesh, pp. 1–6 (2018)

  31. Balda, R.A., Diller, G., Deardorff, E., Doue, J., Hsieh, P.: The HP ECG analysis program. In: Trends in Computer Processed Electrocardiograms. North Holland, Amsterdam, pp. 197–205 (1977)

  32. Ahlstrom, M.L., Tompkins, W.J.: Automated high-speed analysis of Holter tapes with microcomputers. IEEE Trans. Biomed. Eng. 30, 651–657 (1983)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Biomedical Engineering, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh

    Md. Asadur Rahman, Md. Mahmudul Haque Milu, Anika Anjum, Abu Bakar Siddik, Md. Mohidul Hasan Sifat & Mohibur Rahman Chowdhury

  2. Department of Biomedical Engineering, Jashore University of Science and Technology (JUST), Jashore, 7408, Bangladesh

    Farzana Khanam

  3. Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology (KUET), Khulna, 9203, Bangladesh

    Mohiuddin Ahmad

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  1. Md. Asadur Rahman
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  2. Md. Mahmudul Haque Milu
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  3. Anika Anjum
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Correspondence to Md. Asadur Rahman.

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Rahman, M.A., Milu, M.M.H., Anjum, A. et al. A statistical designing approach to MATLAB based functions for the ECG signal preprocessing. Iran J Comput Sci 2, 167–178 (2019). https://doi.org/10.1007/s42044-019-00035-0

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