Stationary wavelet transform based ECG signal denoising method.

Electrocardiogram (ECG) signals are used to diagnose cardiovascular diseases. During ECG signal acquisition, various noises like power line interference, baseline wandering, motion artifacts, and electromyogram noise corrupt the ECG signal. As an ECG signal is non-stationary, removing these noises from the recorded ECG signal is quite tricky. In this paper, along with the proposed denoising technique using stationary wavelet transform, various denoising techniques like lowpass filtering, highpass filtering, empirical mode decomposition, Fourier decomposition method, discrete wavelet transform are studied to denoise an ECG signal corrupted with noise. Signal-to-noise ratio, percentage root-mean-square difference, and root mean square error are used to compare the ECG signal denoising performance. The experimental result showed that the proposed stationary wavelet transform based ECG denoising technique outperformed the other ECG denoising techniques as more ECG signal components are preserved than other denoising algorithms.

[1]  Madhuchhanda Mitra,et al.  Empirical mode decomposition based ECG enhancement and QRS detection , 2012, Comput. Biol. Medicine.

[2]  Susmita Das,et al.  An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter , 2018, Biomed. Signal Process. Control..

[3]  Adrian Basarab,et al.  Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review , 2016, J. Biomed. Informatics.

[4]  Anh Dinh,et al.  Single channel high noise level ECG deconvolution using optimized blind adaptive filtering and fixed-point convolution kernel compensation , 2020, Biomed. Signal Process. Control..

[5]  J. S. Sahambi,et al.  Quantitative analysis of errors due to power-line interference and base-line drift in detection of onsets and offsets in ECG using wavelets , 1997, Medical and Biological Engineering and Computing.

[6]  T. Torfs,et al.  Wireless 3-lead ECG system with on-board digital signal processing for ambulatory monitoring , 2012, 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[7]  Rama Komaragiri,et al.  From Pacemaker to Wearable: Techniques for ECG Detection Systems , 2018, Journal of Medical Systems.

[8]  B. Widrow,et al.  Adaptive noise cancelling: Principles and applications , 1975 .

[9]  Ashish Kumar,et al.  Design of High-Performance ECG Detector for Implantable Cardiac Pacemaker Systems using Biorthogonal Wavelet Transform , 2018, Circuits Syst. Signal Process..

[10]  Sujatha Krishnamoorthy,et al.  A study on ECG signal characterization and practical implementation of some ECG characterization techniques , 2019 .

[11]  Hualou Liang,et al.  Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease , 2005, IEEE Transactions on Biomedical Engineering.

[12]  C. Van Hoof,et al.  Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system , 2012, 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[13]  Hugo Vélez-Pérez,et al.  Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling , 2012, Biomed. Signal Process. Control..

[14]  Patrick E. Mantey,et al.  Digital filtering with applications to electrocardiogram processing , 1968 .

[15]  Haroon Rasheed,et al.  Selection of an optimal mother wavelet basis function for ECG signal denoising , 2014, 17th IEEE International Multi Topic Conference 2014.

[16]  Alper Bozkurt,et al.  Insect–Machine Interface Based Neurocybernetics , 2009, IEEE Transactions on Biomedical Engineering.

[17]  Robert Sutton,et al.  An introduction to wavelet transforms:aA tutorial approach , 2003 .

[18]  Shiv Dutt Joshi,et al.  The Fourier decomposition method for nonlinear and non-stationary time series analysis , 2015, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[19]  Prasanna Kumar Sahu,et al.  Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique , 2018 .

[20]  Rama Komaragiri,et al.  Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure. , 2018, ISA transactions.

[21]  Olaf Dössel,et al.  Comparison of Baseline Wander Removal Techniques considering the Preservation of ST Changes in the Ischemic ECG: A Simulation Study , 2017, Comput. Math. Methods Medicine.

[22]  Danielle S. Bassett,et al.  Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction , 2015, PloS one.

[23]  B. Subramanian.  ECG signal classification and parameter estimation using multiwavelet transform , 2017 .

[24]  Michael Muma,et al.  Motion artifact removal in ECG signals using multi-resolution thresholding , 2012, 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO).

[25]  Rama Komaragiri,et al.  Design of wavelet transform based electrocardiogram monitoring system. , 2018, ISA transactions.

[26]  G. Lamas,et al.  ACC/AHA guidelines for the management of patients with ST-elevation myocardial infarction--executive summary. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to revise the 1999 guidelines for the management of patients wi , 2004, Journal of the American College of Cardiology.

[27]  Malaya Kumar Hota,et al.  A review on ECG filtering techniques for rhythm analysis , 2020, Research on Biomedical Engineering.

[28]  Selcan Kaplan Berkaya,et al.  A survey on ECG analysis , 2018, Biomed. Signal Process. Control..

[29]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[30]  I.Y. Kim,et al.  Notch filter design using theα-scaled sampling kernel and its application to power line noise removal from ECG signals , 2007, 2007 International Conference on Control, Automation and Systems.

[31]  Alireza Mehrnia,et al.  A novel low-complexity digital filter design for wearable ECG devices , 2017, PloS one.

[32]  Rama Komaragiri,et al.  Design of a Biorthogonal Wavelet Transform Based R-Peak Detection and Data Compression Scheme for Implantable Cardiac Pacemaker Systems , 2018, Journal of Medical Systems.

[33]  Minglei Shu,et al.  Sparse ECG Denoising with Generalized Minimax Concave Penalty , 2019, Sensors.

[34]  Xiao-Hua Yu,et al.  An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks , 2013, Neurocomputing.

[35]  Khaled Daqrouq,et al.  ECG Signal Denoising By Wavelet Transform Thresholding , 2008 .

[36]  Rama Komaragiri,et al.  Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems , 2019, Int. J. Circuit Theory Appl..

[37]  Binqiang Chen,et al.  Removal of Power Line Interference From ECG Signals Using Adaptive Notch Filters of Sharp Resolution , 2019, IEEE Access.

[38]  Ashish Kumar,et al.  Design of high performance QRS complex detector for wearable healthcare devices using biorthogonal spline wavelet transform. , 2018, ISA transactions.

[39]  J. van Alsté,et al.  Removal of Base-Line Wander and Power-Line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps , 1985, IEEE Transactions on Biomedical Engineering.

[40]  Maryam Shojaei Baghini,et al.  Motion Artifact Removal in Ambulatory ECG Signal for Heart Rate Variability Analysis , 2019, IEEE Sensors Journal.

[41]  Yongqiang Ye,et al.  Fractional zero-phase filtering based on the Riemann-Liouville integral , 2014, Signal Process..

[42]  Ralf Bousseljot,et al.  Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet , 2009 .

[43]  Ram Bilas Pachori,et al.  An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique , 2020, Biomed. Signal Process. Control..

[44]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[45]  Jean-Marc Vesin,et al.  REWARD: Design, Optimization, and Evaluation of a Real-Time Relative-Energy Wearable R-Peak Detection Algorithm * , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).