A Fast and Simple Adaptive Bionic Wavelet Transform: ECG Baseline Shift Correction

Wang, Ludi; Zhou, Xiaoguang; Xing, Ying; Liang, Siqi

doi:10.1515/cait-2016-0077

Abstract

An ECG baseline shift correction method is presented on the base of the adaptive bionic wavelet transform. After modifying the bionic wavelet transform according to the characteristics of the ECG signal, we propose a novel adaptive BWT algorithm. Using the contaminated and actual data in the MIT-BIH database, the method of fast and simple adaptive bionic wavelet transform can effectively correct the baseline shift under the premise of maintaining the geometric characteristics of the ECG signal. Evaluation of the proposed method shows that the average improvement SNR of FABWT is 2.187 dB more than the CWT-based best case result.

References

1. Clifford, G. D., F. Azuaje, P. McSharry. Advanced Methods and Tools for ECG Data Analysis. Linear Filtering Methods. Chapter 5. Boston, Artech House Publishers, 2006.
Search in Google Scholar Back to article
2. Thakor, N. V., Y. S. Zhu. Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection. – IEEE Transactions on Biomedical Engineering, Vol. 38, 1991, No 8, pp. 785-794.10.1109/10.83591
Search in Google Scholar Back to article
3. Clifford, G. D., F. Azuaje, P. Mc Sharry. Advanced Methods and Tools for ECG Data Analysis. Artech House Publishers, 2006, pp. 164-167.
Search in Google Scholar Back to article
4. Daqrouq, K., et al. ECG Baseline Wander Reduction Using Discrete Wavelet Transform. – Asian Journal of Information Technology, Vol. 4, 2005, No 11, pp. 989-995.
Search in Google Scholar Back to article
5. Kestler, H. A., M. Haschka, W. Kratz et al. Denoising of High-Resolution ECG Signals by Combining the Discrete Wavelet Transform with the Wiener Filter. – Proceedings of Computers in Cardiology, 1998, pp. 233-236.
Search in Google Scholar Back to article
6. Martínez, J. P., et al. A Wavelet-Based ECG Delineator: Evaluation on Standard Database. – IEEE Trans. Biomed. Eng., Vol. 51, 2004, No 4, pp. 570-581.10.1109/TBME.2003.821031
Search in Google Scholar Back to article
7. Singh, B. N., A. K. Tiwari. Optimal Selection of Wavelet Basis Function Applied to ECG Signal Denoising. – Digit. Signal Process, Vol. 3, 2006, No 16, pp. 275-287.10.1016/j.dsp.2005.12.003
Search in Google Scholar Back to article
8. Meyer, C. R., H. N. Keiser. Electrocardiogram Baseline Noise Estimation and Removal Using Cubic Splines and State-Space Computation Techniques. – Computers and Biomedical Research, Vol. 10, 1977, No 5, pp. 459-470.10.1016/0010-4809(77)90021-0
Search in Google Scholar Back to article
9. MacFarlane, P. W., J. Peden, J. Lennox, M. P. Watts, T. D. Lawrie. The Glasgow System. Trends in Computer Processed Electrocardiograms. – In: Proc. of IFIP Working Conference on Trends in Computer-Processed Electrocardiograms, 1977, pp. 143-150.
Search in Google Scholar Back to article
10. Yao, J., Y. T. Zhang. From Otoacoustic Emission Modeling to Bionic Wavelet Transform. – In: Proc. of 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 1, 2000, pp. 314-316.
Search in Google Scholar Back to article
11. Mourad, T., et al. Speech Enhancement Based on Stationary Bionic Wavelet Transform and Maximum a Posterior Estimator of Magnitude-Squared Spectrum. – International Journal of Speech Technology, 2016, pp. 1-14.10.1007/s10772-016-9388-7
Search in Google Scholar Back to article
12. Mourad, T., et al. Bionic Wavelet Based Denoising Using Source Separation. – International Journal of Computers Communications & Control, Vol. 7, 2012, No 3, pp. 529-539.
Search in Google Scholar Back to article
13. Zuo, D. G., et al. Research on a Speech Denoising Method Based on Bionic Wavelet Transform and Fuzzy Reasoning. – Electronic Design Engineering, Vol. 1, 2013, No 51.
Search in Google Scholar Back to article
14. Kemp, D. T., et al. Stimulated Acoustic Emissions from Within the Human Auditory System. – J. Acoust. Soc. Amer., Vol. 64, 1978, No 5, pp. 1386-1391.10.1121/1.382104744838
Search in Google Scholar Back to article
15. Probst, R., et al. A Review of Otoacoustic Emissions. – J. Acoust. Soc. Amer., Vol. 89, 1991, No 5, pp. 2027-2066.10.1121/1.4008971860995
Search in Google Scholar Back to article
16. Lim, D. J., F. Kalinec. Cell and Molecular Basis of Hearing. – Kidney Int. Suppl., Vol. 53, 1998, pp. 104-113.
Search in Google Scholar Back to article
17. Donoho, D. L., et al. De-Noising by Soft-Thresholding. – IEEE Transactions on Information Theory, Vol. 41, 1995, No 3, pp. 613-627.10.1109/18.382009
Search in Google Scholar Back to article
18. Donoho, D. L., I. M. Johnstone. Adapting to Unknown Smoothness via Wavelet Shrinkage. – Journal of the American Statistical Association, Vol. 90, 1995, No 432, pp. 1200-1224.10.1080/01621459.1995.10476626
Search in Google Scholar Back to article
19. Sayadi, O., M. B. Shamsollahi. Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction. – Eurasip Journal on Advances in Signal Processing, Vol. 1, 2007, pp. 1-11.10.1155/2007/41274
Search in Google Scholar Back to article
20. The MIT-BIH Arrhythmia Database. https://physionet.org/physiobank/database/mitdb/
Search in Google Scholar Back to article
21. The MIT-BIH Noise Stress Test Database. https://physionet.org/physiobank/database/nstdb/
Search in Google Scholar Back to article

A Fast and Simple Adaptive Bionic Wavelet Transform: ECG Baseline Shift Correction

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