In
this
work,
we
propose
a
novel
approach
for
identifying
schizophrenia
using
an
entropy
difference
(ED)-
based
electroencephalogram
(EEG)
channel
selection
algorithm.
At
the
core
of
our
is
ED-based
algorithm,
which
selects
most
significant
EEG
channels
that
contain
discriminative
information
detection
values.
This
process
not
only
but
also
reduces
computational
complexity
detection.
After
selecting
channels,
decompose
selected
signals
into
subbands
discrete
wavelet
transform
(DWT).
Furthermore,
extract
symmetrically-weighted
local
binary
patterns
to
capture
subband
variations.
The
features
are
then
subjected
support
vector
machine
(SVM)
differentiate
individuals
with
on
their
signals.
proposed
achieves
classification
accuracy
100%
when
from
one
used,
outperforming
existing
approaches
in
Also,
outperforms
entropy-based
Diagnostics,
Год журнала:
2025,
Номер
15(4), С. 434 - 434
Опубликована: Фев. 11, 2025
Artificial
intelligence
(AI)
has
emerged
as
a
transformative
force
in
psychiatry,
improving
diagnostic
precision,
treatment
personalization,
and
early
intervention
through
advanced
data
analysis
techniques.
This
review
explores
recent
advancements
AI
applications
within
focusing
on
EEG
ECG
analysis,
speech
natural
language
processing
(NLP),
blood
biomarker
integration,
social
media
utilization.
EEG-based
models
have
significantly
enhanced
the
detection
of
disorders
such
depression
schizophrenia
spectral
connectivity
analyses.
ECG-based
approaches
provided
insights
into
emotional
regulation
stress-related
conditions
using
heart
rate
variability.
Speech
frameworks,
leveraging
large
(LLMs),
improved
cognitive
impairments
psychiatric
symptoms
nuanced
linguistic
feature
extraction.
Meanwhile,
analyses
deepened
our
understanding
molecular
underpinnings
mental
health
disorders,
analytics
demonstrated
potential
for
real-time
surveillance.
Despite
these
advancements,
challenges
heterogeneity,
interpretability,
ethical
considerations
remain
barriers
to
widespread
clinical
adoption.
Future
research
must
prioritize
development
explainable
models,
regulatory
compliance,
integration
diverse
datasets
maximize
impact
care.
Smart Materials and Structures,
Год журнала:
2024,
Номер
33(8), С. 085012 - 085012
Опубликована: Июнь 26, 2024
Abstract
Deep
learning
models
such
as
convolutional
neural
networks
(CNNs)
encounter
challenges,
including
instability
and
overfitting,
while
predicting
bolt
looseness
in
data-scarce
scenarios.
In
this
study,
we
proposed
a
novel
audio
signal
augmentation
approach
to
classify
the
event
of
data
deficiency
using
CNN
models.
Audio
signals
at
varied
torque
conditions
were
extracted
percussion
method.
was
performed
shifting
scaling
strategies
after
segmenting
signals.
The
unaugmented
augmented
transformed
into
scalograms
continuous
wavelet
transform
train
Upon
training
with
datasets,
promising
improvement
loss
accuracy
recognizing
noticed.
One
significant
observations
from
current
study
is
that
implementation
improved
extrinsic
generalization
ability
looseness.
A
maximum
increase
73.5%
identify
exhibited
compared
without
augmentation.
Overall,
94.5%
unseen
demonstrated
upon
summary,
results
affirm
empowered
predict
data-deficient
scenarios
accurately.
