ECG Noise Cancellation with Recursive Gauss-Seidel Algorithm
Abstract
Electrocardiogram (ECG) signals provide information about heart functions and some cardiac diseases. However, various interferences distort the ECG waveforms during its measurement and transmission can cause inaccurate analysis and diagnosis. So, this unwanted disturbance signals must be eliminated and an acceptable ECG signal must be extracted the noisy ECG recordings. Researchers developed several methods to overcome the undesired noises and interferences contaminated to the ECG recordings. The adaptive filtering techniques have attracted the attention of scientists due to their adaptation mechanism to time-varying nature of undesired signals. Most of the presented adaptive filtering algorithms are gradient-based and have the advantage of simple implementation, but are affected negatively by disturbance signals; for example, they can have slow convergence rates and poor steady-state properties. Least squares-based algorithms are advantageous due to their faster convergence rates and better steady-state properties. In this paper, Recursive Gauss-Seidel (RGS) algorithm, which is an alternative least squares-based method to Recursive Least Squares (RLS) algorithm with less computational complexity, is presented to obtain an acceptable waveform from noisy ECG recordings. The denoising performance of the RGS algorithm is studied and compared to the widely used gradient-based algorithms and the popular RLS algorithm.
Keywords
ECG denoising , Adaptive filtering , Gauss-Seidel , Recursive algorithm
References
- Berkaya, S. K., Uysal, A. K., Gunal, E. S., Ergin, S., Gunal, S., and Gulmezoglu, M. B. (2018). A survey on ECG analysis. Biomedical Signal Processing and Control, 43, 216-235. http://dx.doi.org/10.1016/j.bspc.2018.03.003
- Bose, T. (2004). Digital signal and image processing. Hoboken, NJ: John Wiley & Sons.
- Chatterjee, S., Thakur, R. S., Yadav, R. N., Gupta, L., and Raghuvanshi, D. K. (2020). Review of noise removal techniques in ECG signals. IET Signal Processing, 14(9), 569-590. http://dx.doi.org/10.1049/iet-spr.2020.0104
- Clifford, G. D., Azuaje, F., and McSharry, P. E. (Eds.). (2006). Advanced methods and tools for ECG data analysis. Norwood, MA: Artech House.
- Faiz, M. M. U., and Kale, I. (2022). Removal of multiple artifacts from ECG signal using cascaded multistage adaptive noise cancellers. Array, 14, 1-9. http://dx.doi.org/10.1016/j.array.2022.100133
- Gowri, T., Kumar, P. R., and Reddy, D. V. R. K. (2014). An efficient variable step size least mean square adaptive algorithm used to enhance the quality of electrocardiogram signal. In S. Thampi, A. Gelbukh, and J. Mukhopadhyay (Ed.), Advances in signal processing and intelligent recognition systems (pp. 463-475). Switzerland: Springer International Publishing.
- Gowri, T., Kumar, P. R., Reddy, D. V. R. K., and Rahman, M. Z. U. (2015). Denoising artifacts from cardiac signal using normalized variable step size LMS algorithm. Sensors & Transducers Journal, 187(4), 138-145.
- Gowri, T., Kumar, P. R., and Reddy, D. V. R. K. (2017). Performance of variable step size LMS adaptive algorithm for the removal of artifacts from electrocardiogram using DSP processor. International Conference on Intelligent Sustainable Systems (ICISS) (pp. 342-346). Palladam, India.
- Hatun, M., and Koçal, O. H. (2012). Recursive Gauss-Seidel algorithm for direct self‐tuning control. International Journal of Adaptive Control and Signal Processing, 26(5), 435-450. http://dx.doi.org/10.1002/acs.1296
- Haykin, S. (2002). Adaptive filter theory (4th ed.). Upper Saddle River, NJ: Prentice-Hall.
