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Signal Artifacts and Techniques for Artifacts and Noise Removal

Part of the Intelligent Systems Reference Library book series (ISRL,volume 192)

Abstract

Biosignals have quite low signal-to-noise ratio and are often corrupted by different types of artifacts and noises originated from both external and internal sources. The presence of such artifacts and noises poses a great challenge in proper analysis of the recorded signals and thus useful information extraction or classification in the subsequent stages becomes erroneous. This eventually results either in a wrong diagnosis of the diseases or misleading the feedback associated with such biosignal-based systems. Brain-Computer Interfaces (BCIs) and neural prostheses are among the popular ones. There have been many signal processing-based algorithms proposed in the literature for reliable identification and removal of such artifacts from the biosignal recordings. The purpose of this chapter is to introduce different sources of artifacts and noises present in biosignal recordings, such as EEG, ECG, and EMG, describe how the artifact characteristics are different from signal-of-interest, and systematically analyze the state-of-the-art signal processing techniques for reliable identification of these offending artifacts and finally removing them from the raw recordings without distorting the signal-of-interest. The analysis of the biosignal recordings in time, frequency and tensor domains is of major interest. In addition, the impact of artifact and noise removal is examined for BCI and clinical diagnostic applications. Since most biosignals are recorded in low sampling rate, the noise removal algorithms can be often applied in real time. In the case of tensor domain systems, more care has to be taken to comply with real time applications. Therefore, in the final part of this chapter, both quantitative and qualitative measures are demonstrated in tables and the algorithms are assessed in terms of their computational complexity and cost. It is also shown that availability of some a priori clinical or statistical information can boost the algorithm performance in many cases.

Keywords

  • Artifact
  • Biosignal
  • ECG
  • EEG
  • Neural signal
  • Noise, etc.

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Courtesy http://www.frontiersin.org/files/Articles/103134/fnsys-08-00144-HTML/image_t/fnsys-08-00144-g003.gif

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Appendix

Appendix

Abbreviation Definition Abbreviation Definition
AAR Automatic Artifact Removal LAMIC Lagged Auto-Mutual Information Clustering
ABP Arterial Blood Pressure LFP Local Field Potential
ADJUST Automatic EEG artifact detector based on the joint use of spatial and temporal features LMS Least Mean Square
ALE Adaptve Line Enhancer LTI Linear Time Invariant
ANC Adaptive Noise Canceller MARA Multiple artifact rejection algorithm
ANFIS Adaptive Neural Fuzzy Inference System MCA Morphological Component Analysis
ANN Artificial Neural Network MCMC Markov Chain Monte Carlo
ARX Auto-Regressive Exogenous MEG Magnetoencephalography
ASR Artifact Subspace Reconstruction MI Motor Imagery
AWGN Additive White Gaussian Noise MMSE Minimum Mean Square Error
BCG Ballistocardiography MUAP Muscle Unit Action Potential
BCI Brain-Computer-Interface MWF multichannel Wiener filter
BioSigKit BioSignal Analysis Kit NB Naïve Bayes
Bio-SP Biosignal-Specific Processing (A toolbox) NEO Non-linear Energy Operator
Biosppy Biosignal Processing in Python (toolbox) OSET Open-source electrophysiological toolbox
BSP biomedical signal processing toolbox PCA Principal Component Analysis
BSS Blind Source Separation PCG Phonocardiogram 
CCA Canonical Correlation Analysis PFEIFER Preprocessing Framework of Electrograms Intermittently Fiducialized from Experimental Recordings
CWT Continuous Wavelet Transform PLI Power Line Interference
DWT Discrete Wavelet Transform PPG Photoplethysmogram
EAP Extracellular Action Potential PRANA Polygraphic Recording Analyzer
ECG/EKG Electrocardiography PSD Power Spectral Density
ecg-kit A Matlab toolbox for cardiovascular (ECG, EKG, ABP, PPG) signal processing REM Rapid Eye Movement
ECoG Electrocorticography RLS Recursive Least Square
ECoG Electrocorticography  RMS Root Mean Squares
EEG Electroencephalography SAE Sparse Autoencoders
EEMD Ensemble Empirical Mode Decomposition SCG Seismocardiography
EMD Empirical Mode Decomposition SNR Signal to Noise Ratio
EMG Electromyography SOS Second-Order Statistics
EOG Electrooculography SPT Signal Processing Techniques
ERP Event Related Potential SSS Subspace Signal Separation
FASTER Fully Automated Statistical Thresholding for EEG Artifact Rejection SSVEP Steady State Visual Evoked Potential
FFT Fast Fourier Transform STFT Short Time Fourier Transform
FIR Finite Impulse Response SVD Singular Value Decomposition
FLNN Functional Link Neural Network SVM Support Vector Machine
FOOBI Fourth-order Tensor method SWT Stationary Wavelet Transform
FORCe Fully online and automated artifact removal for BCI TDSEP Temporal De-correlation source SEParation
HCI Human-Computer Interfacing VBA Variational Bayes Approximation
HMI Human-Machine Interfacing W-CCA Wavelet Enhanced CCA
IAP Intracellular Action Potential WFDB WaveForm DataBase
ICA Independent Component Analysis W-ICA Wavelet Enhanced ICA
iEEG intracranial electroencephalography WNN Wavelet Neural Network
IIR Infinite Impulse Response WPD Wavelet Packet Decomposition
k-NN K- Nearest Neighbor WT Wavelet Transform

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Islam, M.K., Rastegarnia, A., Sanei, S. (2021). Signal Artifacts and Techniques for Artifacts and Noise Removal. In: Ahad, M.A.R., Ahmed, M.U. (eds) Signal Processing Techniques for Computational Health Informatics. Intelligent Systems Reference Library, vol 192. Springer, Cham. https://doi.org/10.1007/978-3-030-54932-9_2

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