Review of Motion Artifacts Removing Techniques for Wireless Electrocardiograms

A certain class of wireless devices such as electrocardiogram (ECG), electromyogram (EMG) and electroencephalogram (EEG), are very useful for telemedicine because they enable the free movement of patients while vital biophysical measurements are taken from them. However, these devices are very sensitive to motion artifacts - electric potentials generated due to the undesirable movement of electrodes on the surface of the skin or the change in the skin impedance. In this paper we examine the scope and usefulness of different types of model-based signal processing and dimensionality reduction techniques to model and reason about motion artifacts. While the techniques we review are applicable for a wide range of signals, we limit our analysis, nevertheless, to wireless electrocardiograms, so that we can base our investigation on experimental data.

[1]  R. Rangayyan,et al.  Biomedical Signal Analysis , 2015 .

[2]  Pablo F. Diez,et al.  Adaptive Filtering for Epileptic Event Detection in the EEG , 2019, Journal of Medical and Biological Engineering.

[3]  Waltenegus Dargie,et al.  Extraction of Motion Artifacts from the Measurements of a Wireless Electrocardiogram using Tensor Decomposition , 2019, 2019 22th International Conference on Information Fusion (FUSION).

[4]  Mohammad B. Shamsollahi,et al.  Fetal electrocardiogram R-peak detection using robust tensor decomposition and extended Kalman filtering , 2013, Computing in Cardiology 2013.

[5]  David A. Tong,et al.  Adaptive reduction of motion artifact in the electrocardiogram , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[6]  Anil Kumar,et al.  Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression , 2016, Comput. Methods Programs Biomed..

[7]  D. Chitra,et al.  An Efficient Adaptive Filter Architecture for Improving the Seizure Detection in EEG Signal , 2016, Circuits Syst. Signal Process..

[8]  Roberto Guerrieri,et al.  Active Electrode IC for EEG and Electrical Impedance Tomography With Continuous Monitoring of Contact Impedance , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[9]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[10]  P. Regalia Adaptive IIR Filtering in Signal Processing and Control , 1994 .

[11]  Hong He,et al.  Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality reduction and clustering , 2017, Appl. Soft Comput..

[12]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[13]  Nurettin Acir,et al.  A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis , 2017, Expert Syst. Appl..

[14]  Adrian Burns,et al.  SHIMMER™ – A Wireless Sensor Platform for Noninvasive Biomedical Research , 2010, IEEE Sensors Journal.

[15]  Chandan Chakraborty,et al.  Application of principal component analysis to ECG signals for automated diagnosis of cardiac health , 2012, Expert Syst. Appl..

[16]  Waltenegus Dargie Tensor-Based Resource Utilization Characterization in a Large-Scale Cloud Infrastructure , 2019, UCC.

[17]  Waltenegus Dargie,et al.  Application of SVD for Removing Motion Artifacts from the Measurements of a Wireless Electrocardiogram , 2019, 2019 22th International Conference on Information Fusion (FUSION).

[18]  Iñaki Romero,et al.  Comparative study of algorithms for ECG segmentation , 2017, Biomed. Signal Process. Control..

[19]  Kuan Jen Lin,et al.  ECG Data Encryption Then Compression Using Singular Value Decomposition , 2018, IEEE Journal of Biomedical and Health Informatics.

[20]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[21]  M. Rosenblatt A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION. , 1956, Proceedings of the National Academy of Sciences of the United States of America.

[22]  M.G. Pecht,et al.  Reduction of Skin Stretch Induced Motion Artifacts in Electrocardiogram Monitoring Using Adaptive Filtering , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Arnaud Delorme,et al.  Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition , 2018, NeuroImage.

[24]  Andrzej Cichocki,et al.  Fast Alternating LS Algorithms for High Order CANDECOMP/PARAFAC Tensor Factorizations , 2013, IEEE Transactions on Signal Processing.

[25]  L. G. Sison,et al.  Adaptive noise cancelling of motion artifact in stress ECG signals using accelerometer , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[26]  Jan R. Wessel Testing Multiple Psychological Processes for Common Neural Mechanisms Using EEG and Independent Component Analysis , 2016, Brain Topography.

[27]  Juliane Junker,et al.  Medical Instrumentation Application And Design , 2016 .

[28]  Lieven De Lathauwer,et al.  Decompositions of a Higher-Order Tensor in Block Terms - Part II: Definitions and Uniqueness , 2008, SIAM J. Matrix Anal. Appl..

[29]  Jan Nedoma,et al.  Non-Invasive Fetal Monitoring: A Maternal Surface ECG Electrode Placement-Based Novel Approach for Optimization of Adaptive Filter Control Parameters Using the LMS and RLS Algorithms , 2017, Sensors.

[30]  Samarendra Dandapat,et al.  Third-order tensor based analysis of multilead ECG for classification of myocardial infarction , 2017, Biomed. Signal Process. Control..

[31]  Bor-Shyh Lin,et al.  Novel Active Comb-Shaped Dry Electrode for EEG Measurement in Hairy Site , 2015, IEEE Transactions on Biomedical Engineering.