Bioengineering,
Год журнала:
2024,
Номер
11(5), С. 464 - 464
Опубликована: Май 7, 2024
Given
its
detrimental
effect
on
the
brain,
alcoholism
is
a
severe
disorder
that
can
produce
variety
of
cognitive,
emotional,
and
behavioral
issues.
Alcoholism
typically
diagnosed
using
CAGE
assessment
approach,
which
has
drawbacks
such
as
being
lengthy,
prone
to
mistakes,
biased.
To
overcome
these
issues,
this
paper
introduces
novel
paradigm
for
identifying
by
employing
electroencephalogram
(EEG)
signals.
The
proposed
framework
divided
into
various
steps.
begin,
interference
artifacts
in
EEG
data
are
removed
multiscale
principal
component
analysis
procedure.
This
cleaning
procedure
contributes
information
quality
improvement.
Second,
an
innovative
graphical
technique
based
fast
fractional
Fourier
transform
coefficients
devised
visualize
chaotic
character
complexities
elucidates
properties
regular
alcoholic
Third,
thirty-four
features
extracted
interpret
signals'
haphazard
behavior
differentiate
between
trends.
Fourth,
we
propose
ensembled
feature
selection
method
obtaining
effective
reliable
group.
Following
that,
study
many
neural
network
classifiers
choose
optimal
classifier
building
efficient
framework.
experimental
findings
show
suggested
obtains
best
classification
performance
recurrent
(RNN),
with
97.5%
accuracy,
96.7%
sensitivity,
98.3%
specificity
sixteen
selected
features.
aid
physicians,
businesses,
product
designers
develop
real-time
system.
Abstract
Advanced
technologies
that
can
establish
intimate,
long‐lived
functional
interfaces
with
neural
systems
have
attracted
increasing
interest
due
to
their
wide‐ranging
applications
in
neuroscience,
bioelectronic
medicine,
and
the
associated
treatment
of
neurodegenerative
diseases.
A
critical
challenge
significance
remains
development
electronic
platforms
offer
conformal
contact
soft
brain
tissue
for
sensing
or
stimulation
activities
chronically
stable
operation
vivo,
at
scales
range
from
cellular‐level
resolution
macroscopic
areas.
This
review
summarizes
recent
advances
this
field,
an
emphasis
on
use
demonstrated
concepts,
constituent
materials,
engineered
designs,
system
integration
address
current
challenges.
The
article
begins
overview
unique
form
factors,
ranging
filamentary
probes
sheets
three‐dimensional
frameworks
alleviating
mechanical
mismatch
between
interface
materials
tissues.
Next,
active
which
utilize
inorganic/organic
semiconductor‐enabled
devices
are
reviewed,
highlighting
various
working
principles
recording
mechanisms
including
capacitively
conductively
coupled
enabled
by
high
transistor
matrices
spatiotemporal
resolution.
subsequent
section
presents
approaches
biological
multiplexed
addressing,
local
amplification
multimodal
high‐channel‐count
large‐scale
a
safe
fashion
provides
multi‐decade
performance
both
animal
models
human
subjects.
summarized
will
guide
future
direction
technology
provide
basis
next‐generation
chronic
high‐performance
operation.
IEEE Sensors Journal,
Год журнала:
2024,
Номер
24(8), С. 12840 - 12852
Опубликована: Март 5, 2024
Evaluating
sleep
quality
through
reliable
staging
is
of
paramount
importance.
Although
many
studies
reached
fair
performances
in
stage
classification,
effectively
leveraging
the
spatial–temporal
characteristics
derived
from
multichannel
brain
recordings
remains
challenging.
We
develop
a
novel
temporal
self-attentional
and
adaptive
graph
convolutional
mixed
model
(TS-AGCMM),
comprising
feature
extraction
module
(FEM),
dynamic
time
warping
(DTW)-based
attention
module,
context
(TCM),
(AGCM)
this
study.
First,
FEM
enables
capturing
representative
information
raw
data.
Then,
DTW-based
utilizes
programming
algorithm
to
enhance
spatial
expression
ability
extracted
features.
The
TCM
includes
multihead
mechanisms
that
capture
dependencies.
In
particular,
we
employ
an
named
normalization-based
(NAM),
which
contributing
factors
weights
suppress
less
salient
information.
Meanwhile,
AGCM
can
obtain
optimal
functional
connections
between
polysomnography
(PSG)
channels,
benefit
learning
property
adjacency
matrix.
Finally,
fuse
features
by
concat
operation
prediction
results.
utilize
Montreal
archive
(MASS)
ISRUC-S3
assess
TS-AGCMM.
TS-AGCMM
exhibits
performance
comparable
other
currently
available
approaches
as
per
our
results,
achieving
accuracy
89.1%
81.2%,
macroaveraging
F1-score
84.7%
79.5%,
well
Cohen's
kappa
coefficient
83.9%
75.8%
on
two
databases,
respectively.
Bioengineering,
Год журнала:
2024,
Номер
11(5), С. 464 - 464
Опубликована: Май 7, 2024
Given
its
detrimental
effect
on
the
brain,
alcoholism
is
a
severe
disorder
that
can
produce
variety
of
cognitive,
emotional,
and
behavioral
issues.
Alcoholism
typically
diagnosed
using
CAGE
assessment
approach,
which
has
drawbacks
such
as
being
lengthy,
prone
to
mistakes,
biased.
To
overcome
these
issues,
this
paper
introduces
novel
paradigm
for
identifying
by
employing
electroencephalogram
(EEG)
signals.
The
proposed
framework
divided
into
various
steps.
begin,
interference
artifacts
in
EEG
data
are
removed
multiscale
principal
component
analysis
procedure.
This
cleaning
procedure
contributes
information
quality
improvement.
Second,
an
innovative
graphical
technique
based
fast
fractional
Fourier
transform
coefficients
devised
visualize
chaotic
character
complexities
elucidates
properties
regular
alcoholic
Third,
thirty-four
features
extracted
interpret
signals'
haphazard
behavior
differentiate
between
trends.
Fourth,
we
propose
ensembled
feature
selection
method
obtaining
effective
reliable
group.
Following
that,
study
many
neural
network
classifiers
choose
optimal
classifier
building
efficient
framework.
experimental
findings
show
suggested
obtains
best
classification
performance
recurrent
(RNN),
with
97.5%
accuracy,
96.7%
sensitivity,
98.3%
specificity
sixteen
selected
features.
aid
physicians,
businesses,
product
designers
develop
real-time
system.
