Adaptive Motion Artifact Reduction Based on Empirical Wavelet Transform and Wavelet Thresholding for the Non-Contact ECG Monitoring Systems

Electrocardiogram (ECG) signals are crucial for determining the health status of the human heart. A clean ECG signal is critical in analysis and diagnosis of heart diseases. However, ECG signals are often contaminated by motion artifact noise in the non-contact ECG monitoring systems. In this paper, an ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed. This method consists of five steps, namely, spectrum preprocessing, spectrum segmentation, EWT decomposition, wavelet threshold denoising, and EWT reconstruction. The proposed approach was used to process real ECG signals collected by the non-contact ECG monitoring equipment. The results of quantitative study and analysis indicate that this approach produces a better performance in terms of restorage of QRS complexes of the original ECG with reduced distortion, retaining useful information in ECG signals, and improvement of the signal to noise ratio (SNR) value of the signal. The output results of the practical ECG signal test show that motion artifact in the real recorded ECG is effectively filtered out. The proposed method is feasible for reducing motion artifacts from ECG signals, whether from simulation ECG signals or practical non-contact ECG monitoring systems.

[1]  Hsin-Yi Lin,et al.  Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals , 2014 .

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

[3]  Da Xu,et al.  Crank-Nicolson/quasi-wavelets method for solving fourth order partial integro-differential equation with a weakly singular kernel , 2013, J. Comput. Phys..

[4]  Kehui Sun,et al.  Implication of two-coupled tri-stable stochastic resonance in weak signal detection , 2018 .

[5]  Haiping Wu,et al.  Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction , 2018, Energy Conversion and Management.

[6]  Linzi Yin,et al.  Implication of Two-Coupled Differential Van der Pol Duffing Oscillator in Weak Signal Detection , 2016 .

[7]  Wei Li,et al.  Motion artifact removal based on periodical property for ECG monitoring with wearable systems , 2017, Pervasive Mob. Comput..

[8]  G. De Backer,et al.  Prognostic value of ECG findings for total, cardiovascular disease, and coronary heart disease death in men and women , 1998, Heart.

[9]  Manuel Blanco-Velasco,et al.  ECG signal denoising and baseline wander correction based on the empirical mode decomposition , 2008, Comput. Biol. Medicine.

[10]  Kehui Sun,et al.  Application of a memristor-based oscillator to weak signal detection , 2018, The European Physical Journal Plus.

[11]  Hui Gong,et al.  Energy of Intrinsic Mode Function for Gas-Liquid Flow Pattern Identification , 2012 .

[12]  Zairi Ismael Rizman,et al.  ELECTROCARDIOGRAM NOISE CANCELLATION USING WAVELET TRANSFORM , 2018 .

[13]  Hongming Zhou,et al.  Facile one-step hydrothermal synthesis of PEDOT:PSS/MnO2 nanorod hybrids for high-rate supercapacitor electrode materials , 2018, Ionics.

[14]  Yu Hen Hu,et al.  Power-Line Interference Detection and Suppression in ECG Signal Processing , 2008, IEEE Transactions on Biomedical Engineering.

[15]  Jianqing Wang,et al.  Motion artefact removals for wearable ECG using stationary wavelet transform , 2017, Healthcare technology letters.

[16]  N.V. Thakor,et al.  Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection , 1991, IEEE Transactions on Biomedical Engineering.

[17]  Shing-Hong Liu,et al.  Motion Artifact Reduction in Electrocardiogram Using Adaptive Filter , 2011 .

[18]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[19]  Miaolei He,et al.  A real-time H∞ cubature Kalman filter based on SVD and its application to a small unmanned helicopter , 2017 .

[20]  Kehui Sun,et al.  Fractional fuzzy entropy algorithm and the complexity analysis for nonlinear time series , 2018, The European Physical Journal Special Topics.

[21]  Zhengjia He,et al.  Wheel-bearing fault diagnosis of trains using empirical wavelet transform , 2016 .

[22]  Haiping Wu,et al.  Smart wind speed forecasting using EWT decomposition, GWO evolutionary optimization, RELM learning and IEWT reconstruction , 2018 .

[23]  Hongjian Li,et al.  Facile Synthesis of Nitrogen-Doped Microporous Carbon Spheres for High Performance Symmetric Supercapacitors , 2018, Nanoscale Research Letters.

[24]  Yong Gyu Lim,et al.  Flexible Capacitive Electrodes for Minimizing Motion Artifacts in Ambulatory Electrocardiograms , 2014, Sensors.

[25]  Guowei Cai,et al.  Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line , 2017, Sensors.

[26]  Lin Xu,et al.  Use of power-line interference for adaptive motion artifact removal in biopotential measurements , 2016, Physiological measurement.

[27]  Refet Firat Yazicioglu,et al.  Noncontact ECG Recording System With Real Time Capacitance Measurement for Motion Artifact Reduction , 2014, IEEE Transactions on Biomedical Circuits and Systems.

[28]  Feng Deshan,et al.  Ground penetrating radar numerical simulation with interpolating wavelet scales method and research on fourth-order Runge-Kutta auxiliary differential equation perfectly matched layer , 2016 .

[29]  Ling-an Kong,et al.  Long-term synaptic plasticity simulated in ionic liquid/polymer hybrid electrolyte gated organic transistors , 2017 .

[30]  Jiming Ma,et al.  A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation , 2018 .

[31]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[32]  Ki H. Chon,et al.  Automatic Motion and Noise Artifact Detection in Holter ECG Data Using Empirical Mode Decomposition and Statistical Approaches , 2012, IEEE Transactions on Biomedical Engineering.

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

[34]  Khaled Daqrouq,et al.  The Discrete Wavelet Transform Based Electrocardiographic Baseline Wander Reduction Method for Better Signal Diagnosis , 2018 .

[35]  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.

[36]  Yu Zheng,et al.  Transmission characteristics of planar optical waveguide devices on coupling interface , 2013 .

[37]  Omkar Singh,et al.  ECG signal denoising via empirical wavelet transform , 2017, Australasian Physical & Engineering Sciences in Medicine.