Removal of Artifcats in Electrocardiograms using Savitzky-Golay Filter: An Improved Approach

Document Type : Special Issue: Deep Learning for Visual Information Analytics and Management.

Authors

1 Research Scholar, Department of Electronics & Communication Engineering, GZSCCET, MRSPTU, Bathinda, Punjab, India.

2 , Associate Prof., Department of Electrical Engineering, Punjab Institute of Technology, GTB Garh, Moga (A Constituent College of MRSPTU, Bathinda), Punjab, India.

Abstract

Electrocardiogram (ECG) is a tool used for the electrical analysis of the status of human heart activity. When the ECG signal is recorded, it gets contaminated with different types of noises. So, for accurate analysis, noises must be eliminated from the ECG signal. There are different types of noises that contaminate the characteristics of ECG signal i.e Power line interference, baseline wander, Electromyogram (EMG). In this paper, different techniques have implemented for the removal of noises. A median filter is used for removal of DC component and Savitzky-Golay filter (SG) is used for smoothing noised waveform and then wavelet transform (db4) is used to decompose the ECG signal for removal of various artifacts. Wavelet transform provides the information in frequency and time domain and then thresholding has been applied for the implementation of algorithms in MATLAB. The measured results i.e. SNR(Signal to Noise ratio) and MSE(Mean square error) have been calculated using different databases like MIT-BIH, Long-term ST database, European ST-T database. The results are examined with proposed methods that are better than those reported in the literature.

Keywords

References

Agrawal, S and Gupta. A (2013). Fractal and EMD based removal of baseline wander and power line interference from ECG signals. Comput. Biol. Med., vol. 43, no. 11, pp. 1889–1899.
Ari. S, Das. M. K., and Chacko. A. (2013). ECG signal enhancement using S-Transform. Comput. Biol. Med., vol. 43, no. 6, pp. 649–660.
Elgendi, M. (2018). Less is more in biosignal analysis: Compressed data could open the door to faster and better diagnosis. Diseases, 6 (1), 18.
Elgendi, M. (2018). Merging digital medicine and economics: Two moving aver-ages unlock biosignals for better health. Diseases,vol.6, no.1, p.6.
Eminaga, Coskun. Y. (2018). Hybrid IIR/FIR Wavelet Filter Banks for ECG Signal Denoising. In IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1-4.
Hadji, Salleh. S, Rohani. M., & Kamat. M. (2016). Wavelet-based Performancein De-noising ECG Signal. In Proceedings of the 8th International Conference on Signal Processing Systems ,pp. 148-153.
Izumi, S. et al. (2015). A Wearable Healthcare System with a 13.7 μ A Noise Tolerant ECG Processor.IEEE Transactions on Biomedical Circuits and Systems, vol. 9, no. 5, pp. 733–742.
Izumi, S. et al. (2015). Normally Off ECG SoC with Non-Volatile MCU and Noise Tolerant Heartbeat Detector.IEEE Transactions on Biomedical Circuits and Systems, vol. 9, no. 5, pp. 641–651.
Jain, S. K. and Bhaumik. B. (2017). An Energy Efficient ECG Signal Processor Detecting Cardiovascular Diseases on Smartphone. IEEE Transactions on Biomedical Circuits and Systems, vol. 11, no. 2, pp. 314–323.
Kher, R. (2019). Signal Processing Techniques for Removing Noise from ECG Signals. Journal of Biomedical Engineering and Research, vol 3: 101.
Manocha, A. K, Singh. M. (2015). Optimal Selection of Wavelet Function and Decomposition Level for Removal of ECG Signal Artifacts. Journal of Medical Imaging and Health Informatics, vol. 5, 138–146.
Manocha, A. K, Singh. M. (2016). Statistical analysis of ST segments in ECG signals for detection of ischaemic episodes. Transactions of the Institute of Measurement and Control. 1–12.
Martis, R. J, Acharya. U. R. (2013). ECG beat classification using PCA,LDA,ICA and Discrete Wavelet Transform. Biomedical Signal Processing and Control, 1746-8094.
Oussama, Latif R, Elmansouri, K. (2017). ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform. Biomedical engineering,16:26.
Pal. S, Mitra. M. (2012). Empirical mode decomposition based ECG enhancement and QRS detection. Computer in biology and medicine, 42, 83-92.
Pareschi, F. et al. (2017). Energy Analysis of Decoders for Rakeness-Based Compressed Sensing of ECG Signals.IEEE Transactions on Biomedical Circuits and Systems, vol. 11, no. 6, pp. 1278–1289.
Pater .C. (2005). Methodological considerations in the design of trials for safety assessment of new drugs and chemical entities. Trials 6 (1),1–13.
Patil, R. (2015). Noise Reduction using Wavelet Transform and Singular Vector Decomposition. ScienceDirect, Procedia Computer Science, 54 , 849 – 853.
Poungponsri, S and Yu. X.-H. (2013). An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks. Neuro computing, vol. 117, pp. 206–213.
Sangaiah A. K, Arumugam. M, Bian. G.B. (2019). An Intelligent Learning Approach for Improving ECG Signal Classification and Arrhythmia Analysis. Artificial Intelligence in Medicine.
Verma. A. R. and Singh. Y. (2015). Adaptive tunable notch filter for ECG signal enhancement. Procedia Comput. Sci., vol. 57, pp. 332–337.
Wang, Zhang S. H, Yang. Y. D, Liu. M, Ramirez. B. (2019). Unilateral sensor neural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression. Integrated Computer-Aided Engineering,1-16.
Wang. J, Ye. Y, Pan. X, and Goa. X. (2015). Parallel-type fractional zero-phase filtering for ECG signal denoising. Biomed. Signal Process. Control, 18, 36–41.
Zhang. D, Wang. S. (2019). An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter. Appl. Sci., 9, 4968.

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