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.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Май 2, 2025
Mental
disorders
represent
a
critical
global
health
challenge
that
affects
millions
around
the
world
and
significantly
disrupts
daily
life.
Early
accurate
detection
is
paramount
for
timely
intervention,
which
can
lead
to
improved
treatment
outcomes.
Electroencephalography
(EEG)
provides
non-invasive
means
observing
brain
activity,
making
it
useful
tool
detecting
potential
mental
disorders.
Recently,
deep
learning
techniques
have
gained
prominence
their
ability
analyze
complex
datasets,
such
as
electroencephalography
recordings.
In
this
study,
we
introduce
novel
deep-learning
architecture
classification
of
post-traumatic
stress
disorder,
depression,
or
anxiety,
using
data.
Our
proposed
model,
multichannel
convolutional
transformer,
integrates
strengths
both
neural
networks
transformers.
Before
feeding
model
low-level
features,
input
pre-processed
common
spatial
pattern
filter,
signal
space
projection
wavelet
denoising
filter.
Then
EEG
signals
are
transformed
continuous
transform
obtain
time-frequency
representation.
The
layers
tokenize
by
our
pre-processing
pipeline,
while
Transformer
encoder
effectively
captures
long-range
temporal
dependencies
across
sequences.
This
specifically
tailored
process
data
has
been
preprocessed
transform,
technique
representation,
thereby
enhancing
extraction
relevant
features
classification.
We
evaluated
performance
on
three
datasets:
Psychiatric
Dataset,
MODMA
dataset,
Psychological
Assessment
dataset.
achieved
accuracies
87.40%
89.84%
92.28%
approach
outperforms
every
concurrent
approaches
datasets
used,
without
showing
any
sign
over-fitting.
These
results
underscore
in
delivering
reliable
disorder
through
analysis,
paving
way
advancements
early
diagnosis
strategies.
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.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Ноя. 26, 2024
Alzheimer's
disease
(AD),
a
prevalent
neurodegenerative
disorder,
leads
to
progressive
dementia,
which
impairs
decision-making,
problem-solving,
and
communication.
While
there
is
no
cure,
early
detection
can
facilitate
treatments
slow
its
progression.
Deep
learning
(DL)
significantly
enhances
AD
by
analyzing
brain
imaging
data
identify
biomarkers,
improving
diagnostic
accuracy
predicting
progression
more
precisely
than
traditional
methods.
In
this
article,
we
propose
an
ensemble
methodology
for
DL
models
detect
from
MRIs.
We
trained
enhanced
Xception
architecture
once
produce
multiple
snapshots,
providing
diverse
insights
into
MRI
features.
A
decision-level
fusion
strategy
was
employed,
combining
decision
scores
with
RF
meta-learner
using
blending
algorithm.
The
efficacy
of
our
technique
confirmed
the
experimental
findings,
categorize
four
groups
99.14%
accuracy.
This
may
help
medical
practitioners
provide
patients
individualized
care.
Subsequent
efforts
will
concentrate
on
enhancing
model's
via
generalization
variety
datasets.
