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Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

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Abstract

This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.

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References

  1. Sayadi, O., & Shamsollahi, M. B. (2006). ECG denoising with adaptive bionic wavelet transform. In Proc. IEEE EMBS (pp. 6597–6600).

  2. Manikandan, M. S., & Dandapat, S. (2007). Wavelet energy based diagnostic distortion measure for ECG. Biomedical Signal Processing and Control, 2, 80–96.

    Article  Google Scholar 

  3. Scott, H. H., & John, V. A. (2008). Automated wavelet denoising of photoacustic signals for circulating melanoma cell detection and burn image. Physics in Medicine and Biology, 53, 227–236.

    Article  Google Scholar 

  4. Prasad, V. V. K. D. V., Siddaiah, P., & Rao, B. P. (2008). A new wavelet based method for denoising of biological signals. IJCSNS International Journal of Computer Science and Network Security, 8, 238–244.

    Google Scholar 

  5. Donoho, D. L. (1995). De-noising by soft thresholding. IEEE Transactions on Information Theory, 41, 613–627.

    Article  Google Scholar 

  6. Donoho, D. L., & Johnstone, I. M. (1994). Ideal spatial adaptation via wavelet shrinkage. Biometrica, 81, 425–455.

    Article  Google Scholar 

  7. Poornachandra, S. (2008). Wavelet-based denoising using subband dependent threshold for ECG signals. Digital Signal Processing, 18, 49–55.

    Article  Google Scholar 

  8. Poornachandra, S., & Kumaravel, N. (2005). Hyper-trim shrinkage for denoising of ECG signal. Digital Signal Processing, 15, 317–327.

    Article  Google Scholar 

  9. Alfaouri, M., & Daqrouq, K. (2008). ECG signal denoising by wavelet transform thresholding. American Journal of Applied Sciences, 5, 276–281.

    Article  Google Scholar 

  10. Novak, D., Frau, D. C., Eck, V., Pérez-Cortés, J. C., & Andreu-García, G. (2000). Denoising electrocardiogram signal using adaptive wavelets. In BIOSIGNAL 2000, Brno, Czech (pp. 18–20).

  11. Singh, B. N., & Tiwari, A. K. (2006). Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Processing, 16, 275–287.

    Article  Google Scholar 

  12. Erçelebi, E. (2004). Electrocardiogram signals de-noising using lifting-based discrete wavelet transform. Computers in Biology and Medicine, 34, 479–493.

    Article  Google Scholar 

  13. Kania, M., Fereniec, M., & Maniewski, R. (2007). Wavelet denoising for multi-lead high resolution ECG signals. Measurement Science Review, 7, 30–33.

    Google Scholar 

  14. Zhang, Y., Wang, L., Gao, Y., Chen, J., & Shi, X. (2007). Noise reduction in Doppler ultrasound signals using an adaptive decomposition algorithm. Medical Engineering & Physics, 29, 699–707.

    Article  Google Scholar 

  15. Magosso, E., Ursino, M., Zaniboni, A., & Gardella, E. (2009). A wavelet-based energetic approach for the analysis of biomedical signals: application to the electroencephalogram and electro-oculogram. Applied Mathematics and Computation, 207, 42–62.

    Article  Google Scholar 

  16. Ferreira da Silva, A. R. (2005). Wavelet denoising with evolutionary algorithms. Digital Signal Processing, 15, 382–399.

    Article  Google Scholar 

  17. Huang, C. L., & Wang, C. J. (2006). A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications, 31, 231–240.

    Article  Google Scholar 

  18. Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3, 95–99.

    Article  Google Scholar 

  19. Sharkand, L.-K., & Chunyang, Y. (2003). Design of optimal shift-invariant orthonormal wavelet filterter banks via genetic algorithm. Signal Processing, 83, 2579–2591.

    Article  Google Scholar 

  20. Ferreira da Silva, A. R. (2001). Evolutionary-based methods for adaptive signal representation. Signal Processing, 81, 927–944.

    Article  Google Scholar 

  21. MIT-BIH database (2010). http://www.physionet.org/physiobank/database/mitdb.

  22. Flexible Intelligence Group (1998). User’s Guide, FlexCI Version 1.0, LLC. http://www.cynapsys.com.

  23. Sameni, R., Shamsollahi, M. B., Jutten, C., & Clifford, G. D. (2007). A nonlinear Bayesian filtering framework for ECG denoising. IEEE Transactions on Biomedical Engineering, 54, 2172–2185.

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Science, Ain Shams University, Abbassia, Cairo, 11566, Egypt

    El-Sayed A. El-Dahshan

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  1. El-Sayed A. El-Dahshan
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Correspondence to El-Sayed A. El-Dahshan.

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El-Dahshan, ES.A. Genetic algorithm and wavelet hybrid scheme for ECG signal denoising. Telecommun Syst 46, 209–215 (2011). https://doi.org/10.1007/s11235-010-9286-2

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  • DOI: https://doi.org/10.1007/s11235-010-9286-2

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