Abstract
Electrocardiogram (ECG) signal helps the physicians in the detection of cardiac-related diseases. Many noises like power line interference (PLI), baseline wander, electromyography (EMG) noise and burst noise are contaminated with the raw signal and corrupt the shape of the waveform which makes the detection faulty. So in recent years, many signal processing methods are proposed for removal of these noise artifacts effectively. In this paper, two hybrid methods, i.e., empirical mode decomposition (EMD) with wavelet transform filtering and EMD with non-local means (NLM) are proposed. The results are analyzed with performance parameters like signal to noise ratio (SNR), mean square error (MSE), and percent root mean square difference (PRD). The results exhibit better performance in hybrid method of EMD with NLM technique.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Schamroth, L.: An Introduction to Electro cardiology. Blackwell Science, London. (1982)
Singh B, Singh P, Budhiraja S.: Various approaches to minimise noises in ECG signal: A survey. Fifth International Conference in Advanced Computing & Communication Technologies (ACCT), IEEE, (2015) 131–137
Mittal A, Rege A.: Design of digital FIR filter implemented with window techniques for reduction of power line interference from ECG signal. International Conference in Computer, Communication and Control (IC4), IEEE, (2015) 1–4
Sultana N, Kamatham Y, Kinnara B.: Performance analysis of adaptive filtering algorithms for denoising of ECG signals. International Conference in Advances in Computing, Communications and Informatics (ICACCI), IEEE (2015) 297–302
Patil, H. T., Holambe, R. S.: New approach of threshold estimation for denoising ECG signal using wavelet transform. In India Conference (INDICON), IEEE (2013) 1–4
Cuomo, S., De Michele, P., Galletti, A., Marcellino, L.: A GPU-Parallel Algorithm for ECG Signal Denoising Based on the NLM Method. 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), IEEE (2016) 35–39
Anapagamini, S. A., Rajavel, R.: Removal of artifacts in ECG using Empirical mode decomposition. International Conference on Communications and Signal Processing (ICCSP), IEEE (2013) 288–292
Biswas, U., Hasan, K. R., Sana, B., Maniruzzaman, M.: Denoising ECG signal using different wavelet families and comparison with other techniques. International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), (2015) 1–6
El Hanine, M., Abdelmounim, E., Haddadi, R., Belaguid, A.: Electrocardiogram signal denoising using discrete wavelet transform. Computer Technology and Application, (2014). 5(2)
Huang, N. E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, Vol. 454, The Royal Society. (1998) 903–995
Buades, A., Coll, B., Morel, J. M.: A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation, (2005) 4(2) 490–530
Samadi, S., Shamsollahi, M. B.: ECG noise reduction using empirical mode decomposition based on combination of instantaneous half period and soft-thresholding. Middle East Conference on Biomedical Engineering (MECBME), IEEE (2014) 244–248
Lahmiri, S.: Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthcare technology letters, 1(3), (2014) 104–109
Lu, J., Liu, H. P., Hsu, C. Y.: Discrete Meyer Wavelet Transform Features For online Hangul Script Recognition. Research Journal of Applied Sciences, Engineering and Technology, 4(20), (2012) 3905–3910
The MIT-BIH ECG Database: www.physionet.org/physiobank/database/mitdb/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Garnaik, S., Rout, N.C., Sethi, K. (2019). Noise Reduction in Electrocardiogram Signal Using Hybrid Methods of Empirical Mode Decomposition with Wavelet Transform and Non-local Means Algorithm. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_57
Download citation
DOI: https://doi.org/10.1007/978-981-10-8055-5_57
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8054-8
Online ISBN: 978-981-10-8055-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)