signals

Review
A Survey on Denoising Techniques of Electroencephalogram
Signals Using Wavelet Transform
Maximilian Grobbelaar 1 , Souvik Phadikar 1, * , Ebrahim Ghaderpour 2 , Aaron F. Struck 1,3 , Nidul Sinha 4 ,
Rajdeep Ghosh 5 and Md. Zaved Iqubal Ahmed 6

1 Department of Neurology, University of Wisconsin Madison, Madison, WI 53792, USA

2 Department of Earth Sciences, Sapienza University of Rome, Piazzale Aldo Moro, 5, 00185 Rome, Italy
3 William S Middleton Veterans Hospital, Madison, WI 53705, USA
4 Department of Electrical Engineering, National Institute of Technology Silchar, Silchar 788010, Assam, India
5 School of Computing Science and Engineering, VIT Bhopal University,
Kotri Kalan 466114, Madhya Pradesh, India
6 Department of Computer Science & Engineering, National Institute of Technology Silchar,
Silchar 788010, Assam, India
* Correspondence: phadikar@neurology.wisc.edu

Abstract: Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle move-
ments widely contaminate the EEG signals. Those unwanted artifacts corrupt the information con-
tained in the EEG signals and degrade the performance of qualitative analysis of clinical applications
and as well as EEG-based brain–computer interfaces (BCIs). The applications of wavelet transform
in denoising EEG signals are increasing day by day due to its capability of handling non-stationary
signals. All the reported wavelet denoising techniques for EEG signals are surveyed in this paper
in terms of the quality of noise removal and retrieving important information. In order to evaluate
the performance of wavelet denoising techniques for EEG signals and to express the quality of
reconstruction, the techniques were evaluated based on the results shown in the respective literature.
Citation: Grobbelaar, M.; Phadikar,
We also compare certain features in the evaluation of the wavelet denoising techniques, such as the
S.; Ghaderpour, E.; Struck, A.F.;
Sinha, N.; Ghosh, R.; Ahmed, M.Z.I.
requirement of reference channel, automation, online, and performance on a single channel.
A Survey on Denoising Techniques of
Electroencephalogram Signals Using Keywords: EEG; wavelet transform; denoising; signal processing
Wavelet Transform. Signals 2022, 3,
577–586. https://doi.org/10.3390/
signals3030035
1. Introduction
Academic Editor: Ran Xiao
From 1929, electroencephalogram (EEG) saw steady progress, and it was in the 1960s
Received: 26 June 2022 that the computerization of EEG started. It was this computerization of EEG that allowed
Accepted: 16 August 2022
for the introduction of automated data analysis in EEG, which came in the form of the
Published: 17 August 2022
fast Fourier transform being used as the basis for power spectral analysis [1]. From these
Publisher’s Note: MDPI stays neutral years of development, we have come to understand that electroencephalography is the
with regard to jurisdictional claims in measurement, amplification, and registration of fluctuating electrical fields produced by
published maps and institutional affil- the brain as a function of time [1]. When neurons inside the brain communicate with each
iations. other, they generate electrical pulses or voltage fluctuations. These electrical pulses or
voltage fluctuations contain information on the communication between different cortices
in the brain, as well as communication to areas such as the peripheral nervous system.
EEG signals have been extensively used to diagnose a variety of brain disorders such as
Copyright: © 2022 by the authors.
epilepsy, Alzheimer’s disease, brain tumors, etc. [2]. Furthermore, EEG has also been used
Licensee MDPI, Basel, Switzerland.
for evaluating sleep patterns of individuals, as well as understanding learning or attention
This article is an open access article
disorders [2]. EEG is recorded using differential amplifiers and it takes two electrical inputs
distributed under the terms and
to display the output as the difference between them [3]. Electrodes consisting of tiny metal
conditions of the Creative Commons
discs are placed on the scalp during the signal recording technique [3]. However, EEG has
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
high temporal resolution but lacks spatial resolution due to the different scales that the
4.0/).
EEG electrodes and neural networks operate on [3]. The neuronal activity being on the

Signals 2022, 3, 577–586. https://doi.org/10.3390/signals3030035 https://www.mdpi.com/journal/signals

Signals 2022, 3 578

scale of micro-volts means that EEG recordings require a level of neural synchronicity in
order to have measurable activity [3]. Even with synchronous activity, the electrodes and
EEG recordings are often subject to artifacts from differing sources. The most common
artifacts seen result from physiological, environmental, and experimental sources [4].
Artifacts are undesired signals that can cause recordings to change and impact the
signal of interest [4]. The most common physiological artifacts found are ocular artifacts,
due to eye blinks and eye movement, muscle artifacts due to inherent flexion and relaxation
of muscles present on the forehead and scalp, and cardiac artifacts due to electrode place-
ment on or near a blood vessel [5]. Because noise sources are so varied and have so many
different properties, most authors concentrate on removing certain types of artifacts. The
removal of artifacts plays a key role in EEG signal processing for both clinical applications
and as well as brain–machine interfaces.
Several denoising techniques have been developed for the purpose of artifact cor-
rection in EEG signals [6–8]. Rejecting artifactual portions of the EEGs is the simplest
way, which deletes epochs containing artifacts. However, removal of artifactual portions
can be a time-consuming process that can lead to significant information loss, which is in
turn detrimental for data analysis [6]. Traditionally, regression and linear filtering-based
analyses are employed to reject artifactual noises from corrupted signals [8]. Due to the
overlap of brain activities and artifactual noises in the spectrum of an EEG signal, filtering
them in either the frequency-domain or the time-domain may result in loss or distortion of
physiological activity [9]. Regression-based methods rely on an extra one or more regressive
channels, which gives rise to a fundamental weakness in that the spectral range of some
artifacts overlaps with the spectral range of an EEG signal [10]. In both the temporal and
frequency domains, wavelet transform-based analysis has been proven to be more efficient
in repairing EEG artifacts while keeping the original EEG signal [11,12].
EEG signals are inherently non-stationary. One of the most widely used approaches
for studying non-stationary signals is the wavelet transform. Its efficiency in transform-
ing a time-domain signal into time-frequency-domain offers major advantages in the
extraction of multiple components of a signal. A wavelet-based method eliminates the
artifacts while retaining the integrity of the EEG signal. Jiang et al. [2] comprehensively
addresses techniques found frequently in EEG artifact rejection and how these techniques
work as individual tools as well as hybrid techniques such as EMD-BSS, wavelet-BSS,
and BSS-Support vector machine. This paper will attempt to serve as a survey focused
specifically on using the wavelet transform as an artifact rejection tool in EEG signals and
will attempt to hone in on some specific wavelet transforms hybrids that have shown
promising performance.

2. Wavelet Denoising
Wavelet transform has been widely used in representing signals in the time-frequency
domain. The wavelet transform decomposes a time-domain signal into its wavelet coeffi-
cients through a mother wavelet function. These coefficients are obtained by performing
shifting and dilation of the mother wavelet as shown in Equation (1):

t−b
 
Ψ a,b (t) = Ψ (1)
a

where a is the scaling parameter and b is the shifting parameter [13,14].

When the noisy version is available, the problem is to restore the information contained
in the signal. In traditional wavelet denoising, the wavelet coefficients are modified by
taking advantage of their local properties and then inverting the transformation to obtain
a clean version of the signal. A basic flow chart of wavelet-based denoising is shown in
Figure 1. An eyeblink corrupted EEG signal of 15 sec duration is shown in the top panel
of Figure 2. The corrupted EEG signal is then decomposed into wavelet coefficients up to
level 5 using Daubechies wavelet with the vanishing moment as 4, and the corresponding
detail and approximation coefficients are shown in the Figure 3. After thresholding both
 Signals 2022, 3, FOR PEER REVIEW wavelet coefficients up to level 5 using Daubechies wavelet with the vanishing 3 moment
as 4, and the corresponding detail and approximation coefficients are shown in the Figure
3. After thresholding both the detail and approximation coefficients, the corrected
transformation to obtain
coefficients are useda clean versionoperation,
in inverse of the signal. and
A basic
anflow chart of wavelet-based
artifact-free EEG signal is shown in the
denoising is shown in Figure 1. An eyeblink corrupted EEG signal of 15 sec duration is
Signals 2022, 3 middle panel of Figure 2 in black.
shown in the top panel of Figure 2. The corrupted EEG signal is then decomposed into
579
wavelet coefficients up to level 5 using Daubechies wavelet with the vanishing moment
as 4, and the corresponding detail and approximation coefficients are shown in the Figure
3. After thresholding both the detail and approximation coefficients, the corrected
the detailareand
coefficients usedapproximation coefficients,
in inverse operation, the corrected
and an artifact-free EEG signalcoefficients are used in inverse
is shown in the
operation,
middle and
panel of an artifact-free
Figure 2 in black. EEG signal is shown in the middle panel of Figure 2 in black.

Figure
Figure 1.
1. Basic
Basicflow
flow chart
chart of
of wavelet-based
wavelet-based signal
signal denoising.
denoising.
Figure 1. Basic flow chart of wavelet-based signal denoising.

Figure 2. Eyeblink corrupted EEG signal in red, corrected EEG signal based on DWT in black and
SWT in blue.

Generally, there are two types of wavelet transform: one is the discrete wavelet
transform (DWT) and another is a continuous wavelet transform (CWT) [15]. DWT is
Figure
Figure 2.
regarded
2.asEyeblink corrupted
a non-redundant
Eyeblink andEEG
corrupted signal
extremely
EEG in
in red,
red, corrected
signaldynamic EEG
EEG signal
wavelet transform
corrected based
based on
on DWT
for obtaining
signal DWT in
in black
black and
and
SWT in blue.
SWT in blue.

Generally,
Generally, there
there are
are two
two types
types of wavelet transform:
of wavelet transform: one
one is
is the
the discrete
discrete wavelet
wavelet
transform
transform (DWT) and another is a continuous wavelet transform (CWT) [15]. DWT is
(DWT) and another is a continuous wavelet transform (CWT) [15]. DWT is
regarded
regarded asas aa non-redundant
non-redundant andand extremely
extremely dynamic
dynamic wavelet
wavelet transform
transform forfor obtaining
obtaining
wavelet representation of signal [16,17]. In DWT, the signal is passed through a half-band
high-pass and a half-band low-pass filter, resulting in detail coefficients and approximate
coefficients, respectively. The process continues until the expected frequency is obtained.
The time-variance of DWT is a severe flaw, which is especially critical in statistical signal
processing applications such as EEG [18]. The stationary wavelet transform (SWT) solves
the DWT’s translation invariance problem; however, it has redundant information and is
sluggish [19]. The filter at each stage is the design difference between DWT and SWT [20].
At each level of decomposition, the approximate and detail sequences are the same length
as the original sequence. Wavelet coefficients extracted through SWT are shown in Figure 4,
and the corrected EEG signal is illustrated in the bottom panel of Figure 2 in blue.
 coefficients, respectively. The process continues until the expected frequency is obtained.
The time-variance of DWT is a severe flaw, which is especially critical in statistical signal
processing applications such as EEG [18]. The stationary wavelet transform (SWT) solves
the DWT’s translation invariance problem; however, it has redundant information and is
sluggish [19]. The filter at each stage is the design difference between DWT and SWT [20].
Signals 2022, 3 At each level of decomposition, the approximate and detail sequences are the same 580 length
as the original sequence. Wavelet coefficients extracted through SWT are shown in Figure
4, and the corrected EEG signal is illustrated in the bottom panel of Figure 2 in blue.

Figure 3. up
Figure 3. Wavelet coefficients Wavelet coefficients
to level 5 of theup corrupted
to level 5 of the
EEGcorrupted
shown EEG
in shown in the
the top top panel
panel of Figure
of Figure 2 25
Signals 2022, 3, FOR PEER REVIEW using Daubechies wavelet with vanishing moment as 4.
using Daubechies wavelet with vanishing moment as 4.

Figureup
Figure 4. SWT coefficients 4. SWT coefficients
to level up to
5 of the level 5 of the
corrupted EEGcorrupted
shown EEG
in shown in the
the top top panel
panel of Figure
of Figure 2 2
using Daubechies wavelet with vanishing moment as 4.
using Daubechies wavelet with vanishing moment as 4.
3. A Survey on Wavelet Transform-based EEG Denoising Techniques
According to the wavelet denoising methods for EEG signals (irrespective of any
kind of artifacts) available in the literature, we grouped all the methods into various
categories and are discussed in this section.

3.1. Wavelet Denoising with Thresholding

Signals 2022, 3 581

3. A Survey on Wavelet Transform-based EEG Denoising Techniques

According to the wavelet denoising methods for EEG signals (irrespective of any kind
of artifacts) available in the literature, we grouped all the methods into various categories
and are discussed in this section.

3.1. Wavelet Denoising with Thresholding

The most efficient and widely used wavelet denoising is based on thresholding wavelet
coefficients. This process follows three important steps: (i) wavelet decomposition: the
input signals are decomposed into wavelet coefficients; (ii) thresholding: the wavelet coeffi-
cients are modified according to a threshold; and (iii) reconstruction: modified coefficients
are used in inverse transform to obtain the noise-free signal. Several researchers have used
thresholding wavelet denoising techniques [21–24]. The universal threshold and statistical
threshold functions are efficiently used in wavelet-based EEG denoising.
Krishnaveni et al. [21] proposed automatic detection and elimination of ocular artifacts
(OA) from EEG signals using wavelet transform. They used a DWT with a Haar wavelet
as the basis function to identify the OA zone. Next, identified OA zones are decomposed
into wavelet domain through SWT with Coiflet wavelet with vanishing moment 3 as
a basis function. The identified OAs are removed from EEG using a non-linear time-scale
adaptive denoising technique, which is based on the wavelet shrinkage strategy. The
optimal thresholds are selected based on Stein’s unbiased risk estimate (SURE) and soft-like
thresholding function using gradient-based adaptive algorithm. Instead of thresholding all
of the wavelet coefficients, Zikov et al. [22] performed thresholding on lower-frequency
bands as OAs have very low-frequency characteristics. They estimated the threshold
by simple statistical analysis of baseline EEG. Islam et al. [23] employed a non-negative
garrote shrinkage function during denoising due to its nice tradeoff between soft and hard
threshold and modified universal threshold as:
√
t0j,l = Kα j,l 2 ln N (2)

where N is epoch length and α j,l is the estimated noise variance for the wavelet coefficients
at lth level (w j,l ).
 
median w j,l 
 
α j,l = (3)
0.6745
The K is a new parameter estimated through empirical observations:

K A (0 < K A < 1)
K= (4)
K D (1 < K D < 3)

Recently, Phadikar et al. [24] proposed an automatic eyeblink artifact correction tech-
nique using wavelet transform and metaheuristic algorithms. In their method, the wavelet
coefficients are thresholded in a backward manner to modify only the lower frequency
bands of the observed EEG signals. Further, to make the system fully automatic, the optimal
thresholds are selected through the grey wolf optimizer.

3.2. Hybrid Methods with Wavelet Transform

Recently, researchers have been hybridizing the wavelet transform with other efficient
denoising techniques. For handling the EEG artifacts, independent component analysis
(ICA) is widely employed with wavelet transform [25–28]. ICA is a mathematical model
that decomposes multivariate signals into their subcomponents [29]. In the wavelet-ICA-
based method, the input EEG signals are decomposed into wavelet coefficients. Next, all
the coefficients are used in ICA operation to separate the various sources of EEG in the
time-frequency domain. Then, the artifacted independent components (ICs) are directly
eliminated. Finally, all ICs are used in inverse operation followed by inverse wavelet
transform to obtain the noise-free EEG signals. Zhou et al. [25] provide an example of
 Signals 2022, 3 582

combining wavelet soft-thresholding, whitening method for preprocessing, and ICA for
the removal of EMG and ECG artifacts. While Zhou et al. proposed a methodology for
a wavelet transform-ICA hybrid, Inuso et al. [26] showed that using the wavelet transform
as an integral part in the separation processes with ICA outperforms hybrid methods that
used the wavelet transform as a denoising technique either pre- or post-ICA.
However, direct elimination of ICs may result in huge information loss as artifacted
ICs also contain cerebral activities. Sai et al. [27] modified the wavelet ICA algorithms
and instead of direct elimination of artifacted ICs in wavelet domain, they performed
thresholding on artifacted ICs to maintain the cerebral activities in ICs. Yasoda et al. [28]
introduced increased automation by implementing a fuzz-kernel support vector machine
for identifying artifacts, prior to removal using a wavelet-ICA combination. A basic flow
chart of wavelet-ICA is shown in Figure 5. Later on, Sai et al. [30] proposed an unsupervised
Signals 2022, 3, FOR PEER REVIEW machine-learning-based method combined with Wavelet-ICA to remove EEG artifacts. 7
They also proposed that the techniques that rely on some arbitrarily defined threshold
often fail to accurately identify the signal artifacts in a given dataset.

Signals 2022, 3, FOR PEER REVIEW 7

Basic flow
Figure 5. Basic
Figure flowchart
chartofofwavelet-ICA
wavelet-ICAbased
basedEEG
EEGdenoising.
denoising.

However, wavelet transform is limited to single-channel EEG signals. It may per-

form well for multi-channel EEG signals with the cost of higher computational time and
higher computational complexity as performing a wavelet transform channel by channel
is a laborious process. To overcome the issue with performing the wavelet transform
for multi-channel EEG, a new hybrid method, ICA-wavelet, was developed in the litera-
ture [31,32]. In the ICA-wavelet-based methods, the multi-channel EEG signals were used
in ICA to decompose into ICs. Then, artifacted ICs are identified and decomposed into
wavelet coefficients. Wavelet coefficients are then thresholded using a universal threshold
or a statistical threshold with a hard or soft thresholding function. Finally, clean multi-
channel EEG signals are achieved by performing inverse operation of wavelet transform
Figure 5. Basic flow chart of wavelet-ICA based EEG denoising.
followed by ICA. A basic flow chart of ICA-wavelet based method is shown in Figure 6.

Figure 6. ICA-wavelet-based EEG denoising.

3.3. Other Wavelet Transform-Based Method

Besides ICA, empirical ensemble mode decomposition (EEMD) and canonical
correlation analysis (CCA) can also be combined with wavelet transform for successful
removal of artifacts from EEG signals [33–36]. When SWT and EEMD are studied
separately, they both appear to be effective in artifact removal from EEG recordings. One
cause could be that the EEMD approach decomposes the signal into frequency and
amplitude components [33]. This method does not confine the artifacts to a specific level.
As a result, the EEMD’s assumption of artifacts with larger amplitude at a specific
decomposition
Figure level mayEEG
ICA-wavelet-based
Figure 6. ICA-wavelet-based notdenoising.
EEG be correct. The CCA algorithms are used to segregate
denoising.
components depending on the EEG sources, such that the artifact component is
considered
3.3. as a single
Other Wavelet CC with a random
Transform-Based Method distribution that can be removed easily [33,34].
Mowla et al. [33] showed that the combination of CCA-SWT coupled with a second-order
Besides ICA, empirical ensemble mode decomposition (EEMD) and canonical
blind identification and SWT had significant improved performance in removing EOG
correlation analysis (CCA) can also be combined with wavelet transform for successful
and EMG artifacts. A combination of CCA-WT can yield improved results compared to
removal of artifacts from EEG signals [33–36]. When SWT and EEMD are studied
using them separately; however, adding in an initial decomposition using EEMD yields
Signals 2022, 3 583

3.3. Other Wavelet Transform-Based Method

Besides ICA, empirical ensemble mode decomposition (EEMD) and canonical correla-
tion analysis (CCA) can also be combined with wavelet transform for successful removal
of artifacts from EEG signals [33–36]. When SWT and EEMD are studied separately, they
both appear to be effective in artifact removal from EEG recordings. One cause could be
that the EEMD approach decomposes the signal into frequency and amplitude compo-
nents [33]. This method does not confine the artifacts to a specific level. As a result, the
EEMD’s assumption of artifacts with larger amplitude at a specific decomposition level
may not be correct. The CCA algorithms are used to segregate components depending
on the EEG sources, such that the artifact component is considered as a single CC with
a random distribution that can be removed easily [33,34]. Mowla et al. [33] showed that the
combination of CCA-SWT coupled with a second-order blind identification and SWT had
significant improved performance in removing EOG and EMG artifacts. A combination of
CCA-WT can yield improved results compared to using them separately; however, adding
in an initial decomposition using EEMD yields improved performance when coupled with
a support vector machine (SVM), leading to EEMD-CCA-DWT [36].
Chen et al. [37] presented an OA removal technique where Kalman filter is combined
with DWT. In their work, DWT is applied to the raw EEG in identified OA zones to
reconstruct a rough OAs approximation. The Kalman filter is used to optimize the OAs
approximations from the previous step. Finally, the optimized OAs are subtracted from raw
EEG. Bajaj et al. [38] proposed a tunable algorithm for artifacts removal from EEG signals
using wavelet packet decomposition and wavelet filtering. Phadikar et al. [39] proposed
a multi-stage EEG denoising method that combines wavelet packet decomposition (WPD)
with a modified non-local means (NLM) algorithm for muscle artifact identification and
removal. Abdi-Sargezeh et al. [40] introduced two novel methods for the removal of EEG
artifacts. In the first method, the common components among EEG channels were extracted
and eliminated as artifacts, called common component rejection (CCR). In the second
method, wavelet decomposition was employed to decompose the EEG signals, then the
CCR method was applied to remove artifacts in the time-frequency domain, referred to
as automatic wavelet CCR (AWCCR). Dora et al. [41] developed a flexible technique to
remove EEG artifacts in the context with minimal supervision. They proposed a new
wavelet-based method that allows to remove artifacts from single-channel EEG based
on a data-driven renormalization of the wavelet coefficients. Their method is capable of
adaptively attenuating artifacts of a different nature.
There are many other robust wavelet decomposition methods whose performances
are yet to be investigated in processing EEG signals. The reader is referred to [14] for more
details of some of these methods.

4. Comparative Analysis
The methods mentioned above are among the most popular for removing EEG artifacts.
Some of these artifact-reduction techniques limit eye movements and blinking during
data collection or exclude artifact-contaminated trials from the analysis. An exhaustive
comparison among the above-stated methods is presented in Table 1.

Table 1. Comparison of wavelet-based EEG denoising techniques.

Requirement of Can Perform on

Methodology Automatic Online
Reference Channel Single Channel
Wavelet Thresholding No Yes No Yes
Wavelet-ICA No Yes No Yes
ICA-wavelet No Yes No No
EEMD-Wavelet No No No No
EEMD-CCA-Wavelet No No No No
CCR-Wavelet No Yes No No
Signals 2022, 3 584

The majority of EEG-based real applications necessitate real-time signal processing

and are resistant to artifacts. This necessitated that the artifact removal methods used be
both automatic and minimal in computational expense. The term “automated process”
refers to a procedure that may automatically identify and eliminate artifact components
without the need for human participation. The wavelet-thresholding-based technique is
simple and efficient for handling all kind of artifacts in the EEG signals. However, Phadikar
et al. [24] showed that the reconstructed clean EEG signals using the universal threshold
and statistical threshold are distinctly different from true clean EEG signals, even though
the artifacts are removed. If we tune both the threshold calculation, it may reconstruct
near the original shape of the EEG. However, tuning them may make the system unable
to operate automatically. ICA is a widely used technique for the removal of artifacts from
EEG signals. However, it should follow three assumptions: (i) the sources are statistically
independent, (ii) each independent component has a non-gaussian distribution, and (iii)
the mixing system is determined. Hence, it may not perform well for single-channel EEG
signals or fewer channels. The wavelet transform is unable to detect artifacts that overlap
with spectral features fully. The disadvantage of mode mixing is also present in EMD. As
a result, finding a single solution that is both efficient and accurate enough to satisfy all of
the prerequisites is extremely challenging.

5. Conclusions
The cerebral cortex generates EEG signals, which can be distorted by certain external
disturbances. EEG is a highly non-stationary signal that is generally contaminated by
a variety of artifacts. Despite there being a variety of strategies proposed for eliminating un-
wanted artifacts, an artifact-removal method that combines high accuracy with algorithmic
efficiency has yet to be established. This paper summarizes wavelet-based EEG denoising
techniques based on the conclusion made in the published literature. The article mainly
focuses on wavelet-based denoising as it efficiently handles non-stationary signals. The
advantages and limitations of all the mentioned methods have been highlighted. Although
the majority of the removal algorithms perform well, the approaches outlined above have
a variety of drawbacks when used in a specific EEG-based application. Few methods
require a reference channel to improve the performance of artifact removal, which is not
possible in some applications. Wavelet-based methods are quite accurate at removing EEG
artifacts; nevertheless, it suffers from higher computational complexity, which may not be
appropriate for online applications. As a result, there is no best option for removing all
forms of artifacts. Therefore, one of the long-term goals of effective artifact attenuation is to
create an application-specific algorithm that is more efficient in terms of time and accuracy.

Funding: This research received no external funding.

Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

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