Separation Enhancement of Power Line Noise from Human ECG Signal Based on Stone Technique

Abstract:

The cardiac signal is very important for the heart disease diagnosis and evaluation. The noise cancelation represent one of the most preprocessing step in ECG signal processing, usually, this signal is very sensitive and varies with time. The ECG signal is mostly contaminated by different signals like Power line noise signal, Baseline signal and muscle signal. The power line interference signal is the most effected signal on the ECG during data recording. Several papers try to cancel the noise based on different ways and to extract the useful information. In this paper a novel approach based on stone blind source extraction is used to extract the pure ECG signal from raw ECG, the main advantage of the proposed approach compared with the classical technique is to separate all the useful information without filtering or cancelling the suitable data from the recording signal. Real ECG data from MIT-BIH databases is taken and the MATLAB program is used to evaluate the experimental results. The performance of the proposed approach is measured based on SNR and MSE. The main contribution of this paper is to use Stone blind source separation technique as a first time in ECG signal analysis and prove that this method is the best technique compared with conventional ways. The obtained result proves Stone BSS technique is very efficient to remove the power line noise.

• Periodical: Journal of Biomimetics, Biomaterials and Biomedical Engineering (Volume 40)
• Pages: 71-78
• DOI: https://doi.org/10.4028/www.scientific.net/JBBBE.40.71
• Online since: February 2019
• Authors: ahmed abdullah*, Ahmed Ghanim Wadday, Ali A. Abdullah
• Keywords: BSS, ECG, Ensemble Average Technique, Filter, Separation Technique

References

[1] K. K. Patro and P. R. Kumar, De-Noising of ECG raw Signal by Cascaded Window based Digital filters Configuration," presented at the 2015 IEEE Power, Communication and Information Technology Conference (PCITC), Siksha 'O, Anusandhan University, Bhubaneswar, India., (2015).

DOI: 10.1109/pcitc.2015.7438145

[2] M. Butt, et al., Power Line Interference Tracking in ECG Signal using State Space RLS,, presented at the 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA), Melbourne, VIC, Australia, (2013).

DOI: 10.1109/iciea.2013.6566367

[3] C. Z. Zhang and A. K. Abdullah, Discussion of Approach for Extracting Pure EOG Reference Signal from EEG Mixture Based On Wavelet Denoising Technique,, Journal of Biomimetics, Biomaterials and Biomedical Engineering, vol. 23, pp.9-17, (2015).

DOI: 10.4028/www.scientific.net/jbbbe.23.9

[4] Z. Chaozhu, et al., A New Blind Source Separation Method to Remove Artifact in EEG Signals,, presented at the 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control, Shenyang, China, (2013).

DOI: 10.1109/imccc.2013.319

[5] T. Ploysuwan and P. Atsawathawichok, Kernel Modeling on Gaussian Process to Eliminate Power Line Interference for ECG Signal,, presented at the 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Hua Hin, Thailand, (2015).

DOI: 10.1109/ecticon.2015.7207017

[6] P. Comon and C. Jutten, Handbook of Blind Source Separation: Inde pendent Component Analysis and Applications,, Academic Press is an Imprint of Elsevier, p.779–814, (2010).

[7] A. Cichocki and S.-i. Amari, Adaptive blind Signal and Image Processing: Learning Algorithms and Applications vol. 2. New York: John Wiley and Sons, (2005).

[8] Goldberger AL, et al., PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals,, IEEE Eng in Med and Biol vol. 101, (2000).

DOI: 10.1161/01.cir.101.23.e215

[9] U. Biswas and M. Maniruzzaman, Removing Power Line Interference from ECG Signal Using Adaptive Filter and Notch Filter , presented at the International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT) (2014).

DOI: 10.1109/iceeict.2014.6919072

[10] S. M. Hussein, Design and Implementation of AI Controller Based on Brain Computer Interface,, Master, Computer Engineering, Nahrain University, Iraq-Baghdad, (2007).

[11] A. Hyvarinen, et al., Independent Component Analysis. United States: J. Wiley New York, (2001).

[12] T. D. Lagerlund, et al., Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition,, J. Clin. Neurophysiol., vol. 14, pp.73-82, (1997).

DOI: 10.1097/00004691-199701000-00007

[13] J. V. Stone, Blind source separation using temporal predictability,, Neural. Comput., vol. 13, p.1559–1574, (2001).

DOI: 10.1162/089976601750265009