Concurrency and Computation Practice and Experience,
Год журнала:
2025,
Номер
37(4-5)
Опубликована: Фев. 10, 2025
ABSTRACT
The
morphological
characteristics
of
electrocardiograms
(ECGs)
serve
as
a
fundamental
basis
for
diagnosing
arrhythmias.
Convolutional
neural
networks
(CNNs),
leveraging
their
local
receptive
field
properties,
effectively
capture
the
features
ECG
signals
and
have
been
extensively
employed
in
automatic
diagnosis
However,
variability
duration
renders
single‐scale
convolutional
kernels
inadequate
fully
extracting
these
features.
To
address
this
limitation,
study
proposes
multi‐scale
parallel
joint
optimization
network
(MPJO_CNN).
proposed
method
utilizes
varying
scales
to
extract
features,
further
refining
via
computation
implementing
strategy
enhance
classification
performance.
Experimental
results
demonstrate
that
on
MIT‐BIH
arrhythmia
database,
not
only
achieved
state‐of‐the‐art
performance,
with
an
accuracy
99.41%
F1
score
98.09%,
but
also
showed
high
sensitivity
classes
fewer
samples.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Фев. 21, 2024
Parkinson's
disease
(PD)
is
a
progressively
debilitating
neurodegenerative
disorder
that
primarily
affects
the
dopaminergic
system
in
basal
ganglia,
impacting
millions
of
individuals
globally.
The
clinical
manifestations
include
resting
tremors,
muscle
rigidity,
bradykinesia,
and
postural
instability.
Diagnosis
relies
mainly
on
evaluation,
lacking
reliable
diagnostic
tests
being
inherently
imprecise
subjective.
Early
detection
PD
crucial
for
initiating
treatments
that,
while
unable
to
cure
chronic
condition,
can
enhance
life
quality
patients
alleviate
symptoms.
This
study
explores
potential
utilizing
long-short
term
memory
neural
networks
(LSTM)
with
attention
mechanisms
detect
based
dual-task
walking
test
data.
Given
performance
significantly
inductance
by
architecture
training
parameter
choices,
modified
version
recently
introduced
crayfish
optimization
algorithm
(COA)
proposed,
specifically
tailored
requirements
this
investigation.
proposed
optimizer
assessed
publicly
accessible
real-world
gait
dataset,
results
demonstrate
its
promise,
achieving
an
accuracy
87.4187
%
best-constructed
models.
IEEE Access,
Год журнала:
2023,
Номер
11, С. 100052 - 100069
Опубликована: Янв. 1, 2023
An
Electrocardiogram
(ECG)
is
a
non-invasive
test
that
broadly
utilized
for
monitoring
and
diagnosing
the
cardiac
arrhythmia.
irregularity
of
heartbeat
generally
defined
as
arrhythmia,
which
potentially
causes
fatal
difficulties
creates
an
instantaneous
life
risk.
Therefore,
arrhythmia
classification
challenging
task
because
overfitting
issue
caused
by
high
dimensional
feature
space
ECG
signal.
In
this
research,
incorporation
Internet
Medical
Things
(IoMT)
developed
with
artificial
intelligence
to
provide
health
people
who
are
having
work,
time,
time-frequency,
entropy,
nonlinearity
features
deep
from
Convolutional
Neural
Network
(CNN)
extracted
obtain
different
categories
signal
features.
The
Selective
Opposition
(SO)
strategy
based
Artificial
Rabbits
Optimization
(SOARO)
proposed
selecting
optimal
subset
overall
avoid
issue.
chosen
used
improve
done
Auto
Encoder
(AE).
Further,
Shapley
additive
explanations
(SHAP)
model
interpret
classified
output
AE.
MIT-BIH
database
evaluating
SOARO-AE.
performance
SOARO-AE
evaluated
using
accuracy,
sensitivity,
specificity,
recall
F1-Measure.
existing
researches
such
C-LSTM,
DL-LAC-CNN,
CNN-DNN,
MC-ECG,
FC
MEAHA-CNN
evaluate
method.
accuracy
98.89%
when
compared
MEAHA-CNN.
