Abstract
Seizure
detection
devices
(SDDs)
offer
promising
technological
advancements
in
epilepsy
management,
providing
real‐time
seizure
monitoring
and
alerts
for
patients
caregivers.
This
critical
review
explores
user
perspectives
experiences
with
SDDs
to
better
understand
factors
influencing
their
adoption
sustained
use.
An
electronic
literature
search
identified
34
relevant
studies
addressing
common
themes
such
as
usability,
motivation,
comfort,
accuracy,
barriers,
the
financial
burden
of
these
devices.
Usability
emerged
most
frequently
discussed
factor,
caregivers
also
emphasizing
importance
ease
use,
long
battery
life,
waterproof
design.
Although
validated
showed
high
satisfaction,
technical
challenges,
false
negatives,
positives
need
much
improvement.
Motivation
use
was
driven
by
enhanced
safety,
symptom
tracking,
health
care
professional
recommendations.
Comfort
wearability
were
aspects,
users
favoring
lightweight,
breathable,
discreet
designs
long‐term
wear.
Users
reported
“comfortable”
preferring
wrist
or
arm‐worn
term.
Accuracy—particularly
minimizing
negatives—was
a
priority
users.
Barriers
included
device
cost,
limited
insurance
reimbursement,
discomfort,
concerns
about
data
privacy.
Despite
many
willing
SDDs.
Recommendations
from
professionals
significantly
increased
motivation.
highlights
SDD
that
address
regarding
looks,
while
reducing
barriers.
Enhancing
clinical
involvement
tailoring
specific
patient
needs
may
be
crucial
promoting
wider
adoption.
Further
research
is
needed
evaluate
impact
on
quality
life
explore
ways
mitigate
challenges
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 18, 2024
Novel
subcutaneous
electroencephalography
(sqEEG)
systems
enable
prolonged,
near-continuous
cerebral
monitoring
in
real-world
conditions.
Nevertheless,
the
feasibility,
acceptability
and
overall
clinical
utility
of
these
remains
unclear.
We
report
on
longest
observational
study
using
ultra
long-term
sqEEG
to
date.
Typical
absence
seizures
are
underreported.
We
aimed
to
improve
patient
care
using
a
wearable
electroencephalograph
(wEEG)
at
home
and
assess
machine
learning
(ML)
pipeline
for
detection.
Patients
with
typical
absences
used
wEEG
device
12-24
h
1
week
after
antiseizure
medication
(ASM)
adjustments.
Three-hertz
generalized
spike-wave
discharges
(SWDs)
≥
3
s
were
as
surrogates.
After
manual
inspection,
we
the
results
guide
medical
treatment.
The
outcomes
seizure
freedom,
number
of
consecutive
measurements
without
relapse,
side
effects.
Afterward,
ML
on
recordings,
neurologist
reviewed
output.
Review
time
diagnostic
performance
compared
inspection.
Nineteen
patients
(12
female,
median
age
=
24
years)
followed
5
months
(range
1-12).
recording
each
session
was
21.3
10-24).
Fifteen
(79%)
seizure-free
during
last
measurement,
including
seven
11
(63%)
diagnosed
refractory
epilepsy.
Ten
relapsed
1-2
recordings
1-6)
3-Hz
SWDs.
Side
effects
occurred
in
21%
patients.
Manual
file
inspection
identified
806
SWDs
≥3
s.
reduced
neurologist's
review
24-h
from
27
10-45)
4.3
min
.1-10),
sensitivity,
precision,
F1-score,
false
positives
per
hour
.8,
.95,
.87,
.007,
respectively.
Home-based
allows
monitoring
ASM
adjustments,
improving
management.
ML-based
performed
well
crucial
reducing
time.
Journal of Sleep Research,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 3, 2025
Automated
sleep
staging
on
wearable
data
could
improve
our
understanding
and
management
of
epilepsy.
This
study
evaluated
scoring
by
a
deep
learning
model
223
night-sleep
recordings
from
50
patients
measured
in
the
hospital
with
an
electroencephalogram
(EEG)
device.
The
scored
stage
every
30-s
epoch
EEG
data,
we
compared
output
clinical
expert
20
nights,
each
for
different
patient.
Bland-Altman
analysis
examined
differences
automated
both
modalities,
using
mixed-effect
models,
explored
between
without
seizures.
Overall,
found
moderate
accuracy
Cohen's
kappa
standard
(0.73
0.59)
(0.61
0.43)
versus
expert.
F1
scores
also
varied
modalities.
sensitivity
was
very
low
N1.
Moreover,
underestimated
duration
most
macrostructure
parameters
except
N2.
On
other
hand,
seizures
during
admission
slept
more
night
(6.37,
95%
confidence
interval
[CI]
5.86-7.87)
(5.68,
CI
5.24-6.13),
p
=
0.001,
but
spent
time
In
conclusion,
accelerometry
monitor
epilepsy,
approach
can
help
automate
analysis.
However,
further
steps
are
essential
to
performance
before
implementation.
Trial
Registration:
SeizeIT2
trial
registered
clinicaltrials.gov,
NCT04284072.
Journal of Clinical Neurophysiology,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 20, 2025
Purpose:
Outpatient
seizure
monitoring
is
crucial
for
optimizing
treatment
strategies
in
epilepsy;
however,
traditional
approaches
such
as
diaries
and
wearables
have
limitations
accuracy
practicality.
This
study
evaluated
the
adherence
utility
of
an
implanted
subcutaneous
EEG
system
patients
with
focal
temporal
lobe
epilepsy.
Methods:
At
a
tertiary
epilepsy
center,
received
two-channel
ultra-long-term
monitoring.
The
includes
subcutaneously
electrode
data
recording
behind-the-ear
companion
device
power
supply
transmission.
Patient
to
was
using
generalized
estimating
equations,
considering
sex,
daytime/nighttime
periods,
age,
course
measurements.
correlations
between
electrographic
or
diary-recorded
seizures
were
also
assessed.
Results:
Fifteen
adult
(mean
age:
45.6
years,
6
females)
monitored
average
200.6
days,
416
confirmed
13
patients.
median
89.3%
(interquartile
range,
[75.6%,
93.4%]),
females
showing
significantly
higher
than
males
(
β
,
−1.1600;
P
=
0.049).
Seizure
diary
reporting
sensitivity
precision
20.8%
56.4%,
respectively,
compared
seizures.
Adherence
correlated
positively
r
0.40;
P,
0.004),
but
not
reports
−0.22;
0.13).
Conclusions:
Patients
demonstrated
high
reliable
monitoring,
suggesting
that
it
could
serve
valuable
tool
managing
clinical
practice.
IEEE Open Journal of Signal Processing,
Год журнала:
2024,
Номер
5, С. 717 - 724
Опубликована: Янв. 1, 2024
The
diagnosis
of
epilepsy
can
be
confirmed
in-hospital
via
video-electroencephalography
(vEEG).
Currently,
long-term
monitoring
is
limited
to
self-reporting
seizure
occurrences
by
the
patients.
In
recent
years,
development
wearable
sensors
has
allowed
patients
outside
specialized
environments.
application
EEG
devices
for
epileptic
in
ambulatory
environments
still
dampened
low
performance
achieved
automated
detection
frameworks.
this
work,
we
present
results
a
grand
challenge,
organized
as
an
attempt
stimulate
methodologies
seizures
on
EEG.
main
drawbacks
developing
algorithms
lack
data
needed
training
such
provided
participants
with
large
dataset
42
focal
epilepsy,
containing
continuous
recordings
behind-the-ear
(bte)
We
challenged
develop
robust
classifier
based
Additionally,
proposed
subtask
order
motivate
data-centric
approaches
improve
and
models.
An
additional
dataset,
bte-EEG
device,
was
employed
evaluate
work
submitted
participants.
paper,
five
best
scoring
methodologies.
performing
approach
feature-based
decision
tree
ensemble
algorithm
augmentation
Fourier
Transform
surrogates.
organization
challenge
high
importance
improving
analysis
diagnosis,
working
towards
implementing
these
technologies
clinical
practice.
Epilepsy Research,
Год журнала:
2024,
Номер
204, С. 107385 - 107385
Опубликована: Июнь 3, 2024
Long-term
ambulatory
EEG
recordings
can
improve
the
monitoring
of
absence
epilepsy
in
children,
but
signal
quality
and
increased
review
workload
are
a
concern.
We
evaluated
feasibility
around-the-ears
arrays
(cEEGrids)
to
capture
3-Hz
short-lasting
ictal
spike-and-wave
discharges
assessed
performance
automated
detection
software
cEEGrids
data.
compared
patterns
bilateral
synchronisation
between
discharges.
Epileptic Disorders,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 5, 2024
Abstract
Objective
Sunflower
syndrome
is
a
rare
photosensitive
childhood‐onset
epilepsy,
featuring
repetitive
handwaving
events
(HWE)
triggered
by
light.
documentation
of
these
HWE
can
be
difficult
due
to
the
numerous
occurring
daily
and/or
caregivers
who
document
seizures
but
are
not
always
present.
Hence,
seizure
diaries
underreporting.
Methods
We
performed
feasibility
study
in
three
Belgian
individuals
assess
possibility
quantify
wrist‐worn
wearable
device
(Axivity
AX6).
conducted
structured
exercise
aiming
capture
patterns
possible
confounders
controlled
environment.
Subsequently,
patients
wore
for
six
consecutive
days
and
nights
at
home.
Spectral
power
analyses
were
characterize
frequency
signature
different
movements.
Results
The
patient
A
B
showed
homogeneity
narrow‐band
frequencies.
Patient
C
did
experience
any
start
proper
control.
Regarding
HWE,
there
was
higher
spectral
Gyroscope
Z
(Gz)
compared
Gy.
inter‐subject
variability
peaks
3–6
Hz
range.
Computer
analysis
visual
annotation,
without
checking
diary,
detected
71%
if
lasted
longer
than
5
s
(sensitivity
64%).
For
shorter
duration,
detection
rate
50%
seemed
concordant
change
eye
movement
(63%)
36%).
most
obvious
confounder
toothbrushing
(TB).
However,
TB
pattern:
that
is,
or
comparable
Gy
Gz.
There
also
Gz
“waving
hello”.
Significance
show
sensor
Axivity
AX6
detect
distinguish
them
from
real‐world
setting.
