THE ARTIFACT REMOVAL FROM ECG RECORDING BASED ON WAVELET COEFFICIENTS ON INDEPENDENT COMPONENTS OF ICA
164 viewsKeywords:
Artifact removal; Biomedical signals; ECG; Independent component analysis; ICA; Wavelet transform.Abstract
The removal of undesirable components to obtain a clean ECG signal is an important job, which helps to increase the accuracy of the clinical diagnosis process. The ECG recorders usually provide some specific filters during ECG recording to remove artifacts. However, with expecting received ECG in the high accuracy, the basic filters are not enough, because the ECG signal recording's often affected from multiple signal sources with with varying amplitudes and frequencies; such as Respiratory, Electroencephalogram (EEG), electrooculography (EOG), electromyography (EMG)… therefore, conventional filters didn't meet the requirements of removing most of the impacting artifact components, however the previously proposed methods cause distortion of the ECG recording signal when improving noise cancellation. In this study, the author has proposed a new method “the independent component analysis (ICA) - combining wavelet transforms” to remove the abnormal noise to improve the accuracy of ECG signal recording with a correlation value of up to 0.968 compared to the desired response.
References
[1]. D. A.Kabir, C.Shahnaz, “Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains”, Biomedical Signal Processing and Control, Vol.7, No.5, (2012), PP. 481-489, ISSN 1746-8094,
[2]. Mohammed AlMahamdy, H. Bryan Riley, “Performance Study of Different Denoising Methods for ECG Signals”, Procedia Computer Science,(2014), Vol.37, PP. 325-332, ISSN 1877-0509,
[3]. Abächerli R, Schmid HJ. “Meet the challenge of high-pass filter and ST-segment requirements with a DC-coupled digital electrocardiogram amplifier”. J Electrocardiol. (2009), Vol.42, No.6, pp:574-9.
[4]. Venkatachalam, K. L., Herbr, J. E., Herbrandson, J. E., son, & Asirvatham, S. J. “Signals and signal processing for the electrophysiologist”electrogram acquisition Circulation. Arrhythmia And Electrophysiology, (2011), Vol. 4, No.6, pp. 965-73.
[5]. S.Chatterjee, R.S.Thakur, R.N.Yadav, L.Gupta, D.K.Raghuvanshi, “Review of noise removal techniques in ECG signals”, The Institution of Engineering and Technology, (2020), Vol.14, No.9, pp. 569-590.
[6]. N. Rashmi, G. Begum and V. Singh, "ECG denoising using wavelet transform and filters," 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, (2017), pp. 2395-2400
[7]. L.Shoker, S.Sanei, J.A.Chambers, Artifact removal from electro- encephalograms using ahybrid BSS-SV Malgorithm, IEEE Signal Process. Lett, (2005) Vol.12, No.10, pp. 721–724.
[8]. Travis. B.S & Krishna.S.N (2010) “ MRI artifacts and correction strategies”, Imaging Med. (2010),Vol. 4, No. 2, pp. 445–457.
[9]. H. Chen, C. Zhao and J. Yin, “Design and implementation of EEMD-assisted ICA joint denoising scheme for ECG signals”, IOP Conference Series: Materials Science and Engineering, (2019); Vol. 569, No.3; pp:569-575.
[10]. Abbaspour S, Lindén M, Gholamhosseini H. “ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA”. Stud Health Technol Inform. (2015); pp:91-97. PMID: 25980853.
[11]. C.C. Chiu, B.H. Hai, S.J. Yeh, and K.Y.K. Liao (2013) “Recovering EEG Signals: Muscle Artifact Suppression Using Wavelet-Enhanced, Independent Component Analysis Integrated with Adaptive Filter” Biomedical Engineering: Applications, Basis and Communications. Vol. 26, No.5, 2014
[12]. Gargiulo, G. D., Mcewan, A. L., Bifulco, P., Cesarelli, M., Jin, C., Tapson, J., Schaik, A. V. Towards true unipolar ECG recording without the Wilson central terminal (preliminary results). Physiological Measurement, (2013), Vol.34, No.9, pp. 991-1012.
