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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.

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Correspondence to Md. Kafiul Islam .

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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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