Frontiers in Human Neuroscience,
Год журнала:
2024,
Номер
18
Опубликована: Дек. 10, 2024
Schizophrenia
(SZ)
is
a
chronic
mental
disorder,
affecting
approximately
1%
of
the
global
population,
it
believed
to
result
from
various
environmental
factors,
with
psychological
factors
potentially
influencing
its
onset
and
progression.
Discrete
wavelet
transform
(DWT)-based
approaches
are
effective
in
SZ
detection.
In
this
report,
we
aim
investigate
effect
decomposition
levels
our
study,
analyzed
early
detection
using
DWT
across
levels,
ranging
1
5,
different
mother
wavelets.
The
electroencephalogram
(EEG)
signals
processed
DWT,
which
decomposes
them
into
multiple
frequency
bands,
yielding
approximation
detail
coefficients
at
each
level.
Statistical
features
then
extracted
these
coefficients.
computed
feature
vector
fed
classifier
distinguish
between
healthy
controls
(HC).
Our
approach
achieves
highest
classification
accuracy
100%
on
publicly
available
dataset,
outperforming
existing
state-of-the-art
methods.
Journal of Yeungnam Medical Science,
Год журнала:
2024,
Номер
41(4), С. 261 - 268
Опубликована: Сен. 9, 2024
Owing
to
a
lack
of
appropriate
biomarkers
for
accurate
diagnosis
and
treatment,
psychiatric
disorders
cause
significant
distress
functional
impairment,
leading
social
economic
losses.
Biomarkers
are
essential
diagnosing,
predicting,
treating,
monitoring
various
diseases.
However,
their
absence
in
psychiatry
is
linked
the
complex
structure
brain
direct
modalities.
This
review
examines
potential
electroencephalography
(EEG)
as
neurophysiological
tool
identifying
biomarkers.
EEG
noninvasively
measures
electrophysiological
activity
used
diagnose
neurological
disorders,
such
depression,
bipolar
disorder
(BD),
schizophrenia,
identify
Despite
extensive
research,
EEG-based
have
not
been
clinically
utilized
owing
measurement
analysis
constraints.
studies
revealed
spectral
complexity
brainwave
abnormalities
BD,
power
schizophrenia.
no
currently
treatment
disorders.
The
advantages
include
real-time
data
acquisition,
noninvasiveness,
cost-effectiveness,
high
temporal
resolution.
Challenges
low
spatial
resolution,
susceptibility
interference,
interpretation
limit
its
clinical
application.
Integrating
with
other
neuroimaging
techniques,
advanced
signal
processing,
standardized
protocols
overcome
these
limitations.
Artificial
intelligence
may
enhance
biomarker
discovery,
potentially
transforming
care
by
providing
early
diagnosis,
personalized
improved
disease
progression
monitoring.
Advances in medical technologies and clinical practice book series,
Год журнала:
2024,
Номер
unknown, С. 114 - 130
Опубликована: Июнь 28, 2024
This
chapter
explores
the
capability
of
artificial
intelligence
(AI)
in
predicting
development
neurodegenerative
sicknesses,
particular
focusing
on
Alzheimer's
ailment.
The
goal
is
to
recognize
cutting-edge
nation
AI
studies
this
area
and
identify
rising
superior
procedures.
Through
conducting
a
complete
literature
evaluation
reading
existing
research,
authors
spotlight
strengths
barriers
use
for
neurodegeneration
prediction.
Similarly,
they
discuss
role
huge
information,
system
mastering,
deep
mastering
strategies
developing
accurate
reliable
prediction
models.
These
findings
endorse
that
has
capacity
seriously
enhance
early
diagnosis
disease
progression.
We
conclude
with
ability
future
instructions
demanding
situations
unexpectedly
increasing
vicinity
Advances in medical technologies and clinical practice book series,
Год журнала:
2024,
Номер
unknown, С. 293 - 319
Опубликована: Июнь 28, 2024
The
benefits
of
AI,
such
as
its
ability
to
analyse
vast
data
sets,
identify
meaningful
patterns,
make
accurate
predictions,
and
provide
reliable
recommendations
have
proven
very
efficient
in
early
precise
diagnosis
various
neurodegenerative
diseases.
main
aim
is
emphasise
the
potential
machine
learning
artificial
intelligence
advance
disease
evaluation
treatment
planning.
A
brief
description
objectives
methodologies
used
for
intelligent
techniques
clearly
explained
with
suitable
case
studies.
study
also
demonstrates
how
learning,
signal
processing,
computer-aided
diagnostic
technologies
assist
physicians
making
better
clinical
decisions.
This
proposal
outlines
a
research
paper
that
aims
investigate
different
AI
ML
algorithms
are
employed
three
diseases
namely,
Alzheimer's
(AD),
Parkinson's
(PD),
Amyotrophic
lateral
sclerosis
(ALS).
