A new quantum-inspired pattern based on Goldner-Harary graph for automated alzheimer’s disease detection
Cognitive Neurodynamics,
Journal Year:
2025,
Volume and Issue:
19(1)
Published: May 10, 2025
Abstract
Alzheimer's
disease
(AD)
is
a
common
cause
of
dementia.
We
aimed
to
develop
computationally
efficient
yet
accurate
feature
engineering
model
for
AD
detection
based
on
electroencephalography
(EEG)
signal
inputs.
New
method:
retrospectively
analyzed
the
EEG
records
134
and
113
non-AD
patients.
To
generate
multilevel
features,
discrete
wavelet
transform
was
used
decompose
input
EEG-signals.
devised
novel
quantum-inspired
EEG-signal
extraction
function
7-distinct
different
subgraphs
Goldner-Harary
pattern
(GHPat),
selectively
assigned
specific
subgraph,
using
forward-forward
distance-based
fitness
function,
each
block
textural
extraction.
extracted
statistical
features
standard
moments,
which
we
then
merged
with
features.
Other
components
were
iterative
neighborhood
component
analysis
selection,
shallow
k-nearest
neighbors,
as
well
majority
voting
greedy
algorithm
additional
voted
prediction
vectors
select
best
overall
results.
With
leave-one-subject-out
cross-validation
(LOSO
CV),
our
attained
88.17%
accuracy.
Accuracy
results
stratified
by
channel
lead
placement
brain
regions
suggested
P4
parietal
region
be
most
impactful.
Comparison
existing
methods:
The
proposed
outperforms
methods
achieving
higher
accuracy
approach,
ensuring
robustness
generalizability.
Cortex
maps
generated
that
allowed
visual
correlation
channel-wise
various
regions,
enhancing
explainability.
Language: Английский
Diagnosis of Alzheimer's Disease Using Non-Linear Features of ERP Signals through a Hybrid Attention-Based CNN-LSTM Model
Computer Methods and Programs in Biomedicine Update,
Journal Year:
2025,
Volume and Issue:
unknown, P. 100192 - 100192
Published: May 1, 2025
Language: Английский