ECG signal denoising using discrete wavelet transform: A comparative analysis of threshold values and functions
DOI:
https://doi.org/10.18537/mskn.09.01.10Keywords:
ECG signal, denoising, DWT, filtering tresholdAbstract
The electrocardiogram signal (ECG) is a bio-signal used to determine cardiac health. However, different types of noise that commonly accompany these signals can hide valuable information for diagnosing disorders. The paper presents an experimental study to remove the noise in ECG signals using the Discrete Wavelet Transform (DWT) theory and a set of thresholds filters for efficient noise filtering. For the assessment process, we used ECG records from MIT-BIH Arrhythmia database (MITDB) and standardized noise signals (muscle activity and electrode-skin contact) database from the Noise Stress Test database. In addition to the ECG signals a white Gaussian noise present in electrical type signals was added. Furthermore, as a first step we considered baseline wander and power line interference reduction. The metrics used are the Signal-to-Noise Ratio (SNR), the Root Mean Squared Error (RMSE), the Percent Root mean square Difference (PRD), and the Euclidian L2 Norm standard (L2N). Results reveal that there is not a single combination of filtering thresholds (function and value) to minimize all types of noise and interference present in ECG signals. Reason why an ECG denoising algorithm is proposed which allows choosing the appropriate combination (function-value) threshold, where the SNR values were the maximum and the error values were the minimum.
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Addison, P. S. (2005). Wavelet transforms and the ECG: a review. Physiological Measurement, 26(5), 155-199. https://doi.org/10.1088/0967-3334/26/5/R01
Alfaouri, M., Daqrouq, K. (2008). ECG signal denoising wavelet transform thresholding. American Journal of Applied Sciences, 5(3), 276-281.
Awal, A., Mostafa, S., Ahmad, M., Rash, M. (2014). An adaptive level dependent wavelet thresholding for ECG denoising. Biocybernetic and Biomedical Engineering, 34(4), 238-249. https://doi.org/10.1016/j.bbe.2014.03.002
Donoho, D. (1995). De-noising by soft-thresholding. IEEE Transctions on Information Theory, 41, 613-627.
Goldberger, A., Amaral, L., Glass L, Hausdorff, J., Ivanov, P., Mark, R., Mietus, J., Moody, G., Peng, C., Stanley, H. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215-e220. https://doi.org/10.1161/01.CIR.101.23.e215
Georgieva, G., Tcheshmedjiev, K. (2013). Denoising of electrocardiogram data with methods of wavelet transform. International conference on computer system and technologies - CompSysTech’13, University of Ruse, pp. 9-19.
Jing-yi, L., Hong, L., Dong, Y., Yan-sheng, Z. (2016). A new wavelet threshold function and denoising application. Mathematical Problems in Engineering, 2016, 8 p. http://dx.doi.org/10.1155/2016/3195492
Moody, G. B., Mark, R. G. (2001). The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45-50. https://doi.org/10.1109/51.932724
Singh, M., Kumar, R., Kumar, A. (2014). Comparison between different wavelet transforms and thresholding techniques for ECG denoising. IEEE International Conference on Advances in Engineering and Technology Research, Unnao, India, 6 p. htpps://doi.org/ 10.1109/ICAETR.2014.7012899
Sörnmo, L., Laguna, P. (2005). Bioelectrical signal processing in cardiac and neurological applications. Oxford, UK: Academic Press. 688 p.
Tompkins, W. J. (2000). Biomedical digital signal processing. New Jersey, US: Prentice Hall. 378 p.
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