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A new hybrid adaptive combination technique for ECG signal enhancement

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Abstract

This paper proposes a new de-noising system technique which is composed of Adaptive line enhancer (ALE) with the discrete wavelet transform (DWT) in order to improve the demerit of the ALE. A new adaptive algorithm which depends mainly on the second order resemblance between a signal and its delayed version is also derived and proposed for the ALE. Unlike the conventional DWT process where an estimation of a specific threshold is taken into account, here the ALE based proposed adaptive algorithm is exploited to enhance the detail coefficients. Therefore, the entire system works well for canceling Gaussian and non-Gaussian noise. Some experiments are carried out on an ECG signal to show the effectiveness of the proposed system. It illustrates from the simulations that the proposed technique demonstrates spectacular results for separating various noise types from the contaminated ECG signal. Finally, the proposed adaptive algorithm is compared with the leaky least mean square algorithm of the bases of mean square error. It is found that the performance of the proposed algorithm provides faster convergence rate and lower steady-state error. Consequently, the overall proposed system model represents a workable solution for ECG signal enhancement.

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References

  • Akansu, A. N., & Haddad, R. A. (2001). Multiresolution signal decomposition: Transforms, subbands, and wavelets. London: Academic Press.

    MATH  Google Scholar 

  • Akwei-Sekyere, S. (2015). Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis. PeerJ, 3, e1086.

    Article  Google Scholar 

  • Alesanco, A., & García, J. (2010). Clinical assessment of wireless ECG transmission in real-time cardiac telemonitoring. IEEE Transactions on Information Technology in Biomedicine, 14(5), 1144–1152.

    Article  Google Scholar 

  • Arvinti, B., Costache, M., Toader, D., Oltean, M., & Isar, A. (2010). ECG statistical denoising in the wavelet domain. In Electronics and telecommunications (ISETC), 2010 9th international symposium on IEEE (pp. 307–310).‏

  • Cichocki, A., & Amari, S. I. (2002). Adaptive blind signal and image processing: Learning algorithms and applications (Vol. 1). New York: Wiley.

    Book  Google Scholar 

  • Donoho, D. L., & Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage. Journal of the American statistical association, 90(432), 1200–1224.

    Article  MathSciNet  MATH  Google Scholar 

  • Elgendi, M., Eskofier, B., Dokos, S., & Abbott, D. (2014). Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems. PLoS ONE, 9(1), e84018.

    Article  Google Scholar 

  • Farhang-Boroujeny, B. (2013). Adaptive filters: Theory and applications. New York: Wiley.

    Book  MATH  Google Scholar 

  • Gokhale, P. S. (2012). ECG signal de-noising using discrete wavelet transform for removal of 50 Hz PLI noise. International Journal of Emerging Technology and Advanced Engineering, 2(5), 81–85.

    Google Scholar 

  • Harikumar, R., & Shivappriya, S. N. (2013). A novel approach for different morphological characterization of ECG signal. In Proceedings of the fourth international conference on signal and image processing (ICSIP 2012) (pp. 13–23). New Delhi: Springer.‏

  • Haykin, S. S. (2008). Adaptive filter theory. New Delhi: Pearson Education India.

    MATH  Google Scholar 

  • Karayiannis, N., & Venetsanopoulos, A. N. (2013). Artificial neural networks: Learning algorithms, performance evaluation, and applications (Vol. 209). Berlin: Springer.

    MATH  Google Scholar 

  • Lecchi, M., Martinelli, I., Zoccarato, O., Maioli, C., Lucignani, G., & Del Sole, A. (2017). Comparative analysis of full-time, half-time, and quarter-time myocardial ECG-gated SPECT quantification in normal-weight and overweight patients. Journal of Nuclear Cardiology, 24(3), 876–887.

    Article  Google Scholar 

  • Liang, W., Zhang, Y., Tan, J., & Li, Y. (2014). A novel approach to ECG classification based upon two-layered HMMs in body sensor networks. Sensors, 14(4), 5994–6011.

    Article  Google Scholar 

  • Mateo, J., Torres, A. M., García, M. A., & Santos, J. L. (2016). Noise removal in electroencephalogram signals using an artificial neural network based on the simultaneous perturbation method. Neural Computing and Applications, 27(7), 1941–1957.

    Article  Google Scholar 

  • Meireles, A., Figueiredo, L., Lopes, L. S., & Almeida, A. (2016). ECG denoising with adaptive filter and singular value decomposition techniques. In: Proceedings of the ninth international C* conference on computer science & software engineering (pp. 102–105). ACM.

  • Rahman, M. Z. U., Shaik, R. A., & Reddy, D. R. K. (2012). Efficient and simplified adaptive noise cancelers for ECG sensor based remote health monitoring. IEEE Sensors Journal, 12(3), 566–573.

    Article  Google Scholar 

  • Sanei, S., Lee, T. K., & Abolghasemi, V. (2012). A new adaptive line enhancer based on singular spectrum analysis. IEEE Transactions on Biomedical Engineering, 59(2), 428–434.

    Article  Google Scholar 

  • Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49(11), 1225–1231.

    Article  Google Scholar 

  • Vullings, R., Vries, B. D., & Bergmans, J. W. M. (2011). An adaptive Kalman filter for ECG signal enhancement. IEEE Transactions on Biomedical Engineering, 58(4), 1094–1103.

    Article  Google Scholar 

  • Widrow, B., Glover, J. R., McCool, J. M., Kaunitz, J., Williams, C. S., & Hearn, R. H. (1975). Adaptive noise cancelling: Principles and applications. Proceedings of the IEEE, 63(12), 1692–1716.

    Article  Google Scholar 

  • Wu, Y., Rangayyan, R. M., Zhou, Y., & Ng, S. C. (2009). Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Medical Engineering & Physics, 31(1), 17–26.

    Article  Google Scholar 

  • Zeidler, J., Satorius, E., Chabries, D., & Wexler, H. (1978). Adaptive enhancement of multiple sinusoids in uncorrelated noise. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(3), 240–254.

    Article  MATH  Google Scholar 

  • Zhu, Z., Zhang, X., Wan, X., & Wang, Q. (2015). A random-valued impulse noise removal algorithm with local deviation index and edge-preserving regularization. Signal, Image and Video Processing, 9(1), 221–228.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, College of Engineering, University of Babylon, Babil, Iraq

    Awwab Qasim Jumaah Althahab

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  1. Awwab Qasim Jumaah Althahab
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Correspondence to Awwab Qasim Jumaah Althahab.

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Althahab, A.Q.J. A new hybrid adaptive combination technique for ECG signal enhancement. Multidim Syst Sign Process 30, 1309–1325 (2019). https://doi.org/10.1007/s11045-018-0608-y

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