Frontiers in Neuroscience,
Journal Year:
2023,
Volume and Issue:
17
Published: March 16, 2023
Children
with
benign
childhood
epilepsy
centro-temporal
spikes
(BECT)
have
spikes,
sharps,
and
composite
waves
on
their
electroencephalogram
(EEG).
It
is
necessary
to
detect
diagnose
BECT
clinically.
The
template
matching
method
can
identify
effectively.
However,
due
the
individual
specificity,
finding
representative
templates
in
actual
applications
often
challenging.
This
paper
proposes
a
spike
detection
using
functional
brain
networks
based
phase
locking
value
(FBN-PLV)
deep
learning.
To
obtain
high
effect,
this
uses
specific
'peak-to-peak'
phenomenon
of
montages
set
candidate
spikes.
With
(FBN)
are
constructed
(PLV)
extract
features
network
structure
during
discharge
synchronization.
Finally,
time
domain
structural
FBN-PLV
input
into
artificial
neural
(ANN)
Based
ANN,
EEG
data
sets
four
cases
from
Children's
Hospital,
Zhejiang
University
School
Medicine
tested
AC
97.6%,
SE
98.3%,
SP
96.8%.
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(11), P. e31827 - e31827
Published: May 23, 2024
Epilepsy
is
one
of
the
most
common
brain
disorders,
and
seizures
epilepsy
have
severe
adverse
effects
on
patients.
Real-time
seizure
detection
using
electroencephalography
(EEG)
signals
an
important
research
area
aimed
at
improving
diagnosis
treatment
epilepsy.
This
paper
proposed
a
real-time
approach
based
EEG
signal
for
detecting
STFT
Google-net
convolutional
neural
network
(CNN).
The
CHB-MIT
database
was
used
to
evaluate
performance,
received
results
97.74
%
in
accuracy,
98.90
sensitivity,
1.94
false
positive
rate.
Additionally,
method
implemented
manner
sliding
window
technique.
processing
time
just
0.02
s
every
2-s
episode
achieved
average
9.85-
second
delay
each
onset.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Feb. 20, 2024
Abstract
Brain
disorders
pose
a
substantial
global
health
challenge,
persisting
as
leading
cause
of
mortality
worldwide.
Electroencephalogram
(EEG)
analysis
is
crucial
for
diagnosing
brain
disorders,
but
it
can
be
challenging
medical
practitioners
to
interpret
complex
EEG
signals
and
make
accurate
diagnoses.
To
address
this,
our
study
focuses
on
visualizing
in
format
easily
understandable
by
professionals
deep
learning
algorithms.
We
propose
novel
time–frequency
(TF)
transform
called
the
Forward–Backward
Fourier
(FBFT)
utilize
convolutional
neural
networks
(CNNs)
extract
meaningful
features
from
TF
images
classify
disorders.
introduce
concept
eye-naked
classification,
which
integrates
domain-specific
knowledge
clinical
expertise
into
classification
process.
Our
demonstrates
effectiveness
FBFT
method,
achieving
impressive
accuracies
across
multiple
using
CNN-based
classification.
Specifically,
we
achieve
99.82%
epilepsy,
95.91%
Alzheimer’s
disease
(AD),
85.1%
murmur,
100%
mental
stress
Furthermore,
context
naked-eye
78.6%,
71.9%,
82.7%,
91.0%
AD,
stress,
respectively.
Additionally,
incorporate
mean
correlation
coefficient
(mCC)
based
channel
selection
method
enhance
accuracy
further.
By
combining
these
innovative
approaches,
enhances
visualization
signals,
providing
with
deeper
understanding
images.
This
research
has
potential
bridge
gap
between
image
visual
interpretation,
better
detection
improved
patient
care
field
neuroscience.
Sensors,
Journal Year:
2023,
Volume and Issue:
24(1), P. 77 - 77
Published: Dec. 22, 2023
Epilepsy
is
a
chronic
neurological
disease
associated
with
abnormal
neuronal
activity
in
the
brain.
Seizure
detection
algorithms
are
essential
reducing
workload
of
medical
staff
reviewing
electroencephalogram
(EEG)
records.
In
this
work,
we
propose
novel
automatic
epileptic
EEG
method
based
on
Stockwell
transform
and
Transformer.
First,
S-transform
applied
to
original
segments,
acquiring
accurate
time-frequency
representations.
Subsequently,
obtained
matrices
grouped
into
different
rhythm
blocks
compressed
as
vectors
these
sub-bands.
After
that,
feature
fed
Transformer
network
for
selection
classification.
Moreover,
series
post-processing
methods
were
introduced
enhance
efficiency
system.
When
evaluating
public
CHB-MIT
database,
proposed
algorithm
achieved
an
accuracy
96.15%,
sensitivity
96.11%,
specificity
96.38%,
precision
96.33%,
area
under
curve
(AUC)
0.98
segment-based
experiments,
along
96.57%,
false
rate
0.38/h,
delay
20.62
s
event-based
experiments.
These
outstanding
results
demonstrate
feasibility
implementing
seizure
future
clinical
applications.
