Abstract
Despite
decades
of
research,
defining
insomnia
remains
challenging
due
to
its
complex
and
variable
nature.
Various
diagnostic
systems
emphasize
the
chronic
nature
impact
on
daily
functioning,
relying
heavily
patient
self-reporting
limitations
in
objective
measures
such
as
polysomnography
(PSG).
Discrepancies
between
subjective
experiences
PSG
results
highlight
need
for
more
nuanced
approaches,
electroencephalogram
(EEG)
spectral
analysis,
which
reveals
distinct
patterns
high-frequency
activity
individuals
with
insomnia.
This
study
explores
EEG
markers
by
integrating
reports
physiological
markers,
specifically
ORP
(Odds-Ratio-Product)
features,
address
inconsistencies
found
previous
research
clinical
settings.
Qualitative
quantitative
definitions
are
contrasted
differences
sleep
architecture
characteristics.
The
aims
determine
whether
groups
defined
weekly
frequency
duration
symptoms
have
different
distribution
characteristics
best
distinguish
patients
from
controls.
Our
findings
suggest
that
ORP,
a
dependent
variable,
captures
most
significant
independent
variables
across
model.
Elevated
beta
power
indicates
increased
cortical
arousal,
supporting
perspective
hyperarousal
disorder.
Future
should
focus
using
enhance
understanding
disturbances
Comprehensive
evaluation
requires
qualitative,
quantitative,
neurophysiological
data
fully
understand
quality.
ABSTRACT
Underpinned
by
rigorous
clinical
trial
data,
the
use
of
existing
home
sleep
apnoea
testing
is
now
commonly
employed
for
disordered
breathing
diagnostics
in
most
centres
globally.
This
has
been
a
welcome
addition
field
given
considerable
burden
disease,
cost,
and
access
limitations
with
in‐laboratory
polysomnography
testing.
However,
approaches
predominantly
aim
to
replicate
elements
conventional
different
forms
focus
on
estimation
apnoea‐hypopnoea
index.
New,
simplified
technology
screening,
detection/diagnosis,
or
monitoring
expanded
exponentially
recent
years.
Emerging
innovations
go
beyond
simple
single‐night
replication
varying
numbers
signals
setting.
These
novel
have
potential
provide
important
new
insights
overcome
many
transform
disease
diagnosis
management
improve
outcomes
patients.
Accordingly,
current
review
summarises
evidence
study
people
suspected
sleep‐related
disorders,
discusses
emerging
technologies
according
three
key
categories:
(1)
wearables
(e.g.,
body‐worn
sensors
including
wrist
finger
sensors),
(2)
nearables
bed‐embedded
bedside
(3)
airables
audio
video
recordings),
outlines
their
disruptive
role
care.
Journal of Sleep Research,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 6, 2025
'How
much
sleep
does
one
need?'
is
a
critical
question
that
has
been
difficult
to
answer.
The
long
history
of
research
culminated
in
population-derived
normative
values
7
9
h
per
night
avoid
dysfunction.
Such
wide
range
sufficiently
large
cannot
know
what
required
for
any
given
individual.
'Sleep
need'
currently
be
directly
measured,
might
not
represented
by
number
(given
the
multiple
functions
subserves),
and
likely
varies
from
individual-to-individual
day-to-day.
This
said,
concept
should
embraced
can
considered
alongside
more
easily
operationalised
routinely
measured
constructs
'sleep
opportunity'
(e.g.,
time
bed)
ability'
(i.e.,
obtained
sleep,
such
as
duration).
Considering
dynamics
all
three
together
may
drive
greater
understanding
about
health,
insufficiency,
disorder
pathology.
In
this
article,
we
describe
new
theory
called
Sleep
Opportunity,
Need,
Ability
provide
rationale
why
both
theoretical
clinical
value.
Journal of the American Heart Association,
Год журнала:
2023,
Номер
12(24)
Опубликована: Дек. 12, 2023
Excessive
daytime
sleepiness
(EDS),
experienced
in
10%
to
20%
of
the
population,
has
been
associated
with
cardiovascular
disease
and
death.
However,
condition
is
heterogeneous
prevalent
individuals
having
short
long
sleep
duration.
We
sought
clarify
relationship
between
duration
subtypes
EDS
outcomes,
accounting
for
these
subtypes.
Frontiers in Neurology,
Год журнала:
2024,
Номер
15
Опубликована: Май 10, 2024
Objectives
This
study
aimed
to
validate
a
sleep
staging
algorithm
using
in-hospital
video-electroencephalogram
(EEG)
in
children
without
epilepsy,
with
well-controlled
epilepsy
(WCE),
and
drug-resistant
(DRE).
Methods
Overnight
video-EEG,
along
electrooculogram
(EOG)
chin
electromyogram
(EMG),
was
recorded
between
4
18
years
of
age.
Classical
performed
manually
as
ground
truth.
An
end-to-end
hierarchical
recurrent
neural
network
for
sequence-to-sequence
automatic
(SeqSleepNet)
used
perform
automated
three
channels:
C4-A1,
EOG,
EMG.
Results
In
176
stages
were
scored:
47
74
WCE,
55
DRE.
The
5-class
accuracy
the
84.7%
83.5%
those
80.8%
DRE
(Kappa
0.79,
0.77,
0.73
respectively).
Performance
per
stage
assessed
an
F1
score
0.91
wake,
0.50
N1,
0.83
N2,
0.84
N3,
0.86
rapid
eye
movement
(REM)
sleep.
Conclusion
We
concluded
that
tested
has
high
WCE.
acceptable,
but
significantly
lower,
which
could
be
explained
by
tendency
more
time
spent
abundant
interictal
epileptiform
discharges
intellectual
disability
leading
less
recognizable
stages.
REM
time,
however,
affected
DRE,
can
detected
reliably
algorithm.
Clinical
trial
registration
:
ClinicalTrials.gov
,
identifier
NCT04584385.
People
with
insomnia
reporting
poorer
sleep
compared
to
estimates
obtained
from
objective
assessments
is
common
across
both
research
and
clinical
settings.
Typically,
individuals
report
less
more
wakefulness
a
given
opportunity
that
captured
via
methods
(e.g.
polysomnography)
[1–3].
Many
different
terms
have
been
used
describe
this
phenomenon
since
the
1970s
[4],
including
but
not
limited
following:
misperception
[5],
sleep-state
[6],
discrepancy
[7],
subjective-objective
[3],
misestimation
[8],
paradoxical-
[9]
pseudo-insomnia
[4].
The
mechanisms
underlying
are
yet
well
understood
[2]
require
future
inform
developments
in
diagnosis
treatment
(or
management)
of
disorder.
aim
letter
facilitate
such
work
by
highlighting
recent
findings
proposing
new
nomenclature
become
standard
practice
for
describing
phenomenon.
Insomnia
regarded
as
“subjective”
disorder,
which
individuals’
perceptions
daytime
functioning
form
basis
its
assessment,
diagnosis,
treatment.
However,
measurements
may
also
hold
an
important
role.
Clinicians
anecdotally
reported
discussing
between
perceived
objectively
measured
can
be
therapeutic.
Preliminary
[10]
support
anecdotal
evidence,
suggesting
personalized
feedback
about
self-report
(sleep
diary)
(actigraphy)
reduced
on
subsequent
nights
group
40
when
those
who
received
no
[10].
there
knowledge
underlie
implications
management
insomnia.
Abstract
Study
Objectives
This
study
was
designed
to
test
the
utility
of
cardiovascular
responses
as
markers
potentially
different
environmental
noise
disruption
effects
wind
farm
compared
traffic
exposure
during
sleep.
Methods
Twenty
participants
underwent
polysomnography.
In
random
order,
and
at
six
sound
pressure
levels
from
33
dBA
48
in
3
dB
increments,
three
types
two
road
recordings
20-s
duration
were
played
established
N2
or
deeper
sleep,
each
separated
by
20
s
without
noise.
Each
sequence
also
included
a
no-noise
control.
Electrocardiogram
finger
pulse
oximeter
recorded
wave
amplitude
changes
pre-noise
onset
baseline
following
assessed
algorithmically
quantify
magnitude
heart
rate
vasoconstriction
exposure.
Results
Higher
more
likely
induce
drops
amplitude.
Sound
low
39
evoked
response
(Odds
ratio
[95%
confidence
interval];
1.52
[1.15,
2.02]).
Wind
with
modulation
less
evoke
than
other
types,
but
warrants
cautious
interpretation
given
numbers
replications
within
type.
Conclusions
These
preliminary
data
support
that
are
particularly
sensitive
marker
noise-induced
during.
Larger
trials
clearly
warranted
further
assess
relationships
between
recurrent
activation
potential
long-term
health
effects.
Carefully
controlled
studies
of
wind
turbine
noise
(WTN)
and
sleep
are
lacking,
despite
anecdotal
complaints
from
some
residents
in
farm
areas
known
detrimental
effects
other
noises
on
sleep.
This
laboratory-based
study
investigated
the
impact
overnight
WTN
exposure
objective
self-reported
outcomes.
Sixty-eight
participants
(38
females)
aged
(mean
±
SD)
49.2
19.5
were
recruited
four
groups;
N
=
14,
living
<10
km
a
reporting
related
disruption;
18,
no
road
traffic
noise-related
18
control
quiet
rural
area.
All
underwent
in-laboratory
polysomnography
during
full-night
conditions
random
order:
night
(19
dB(A)
background
laboratory
noise),
continuous
(25
dB(A))
throughout
night;
only
periods
established
sleep;
wake
or
light
N1
Group,
condition,
interaction
measures
quantity
quality
examined
via
linear
mixed
model
analyses.
There
significant
condition
group-by-noise
polysomnographic
diary
determined
outcomes
(all
ps
>
.05).
These
results
do
not
support
that
at
25
impacts
with
without
prior
habitual
disruption.
findings
rule
out
higher
levels
potential
more
sensitive
markers
ACTRN12619000501145,
UTN
U1111-1229-6126.
Establishing
physiological
disruption
characteristics
disturbances
https://www.anzctr.org.au/.
was
prospectively
registered
Australian
New
Zealand
Clinical
Trial
Registry.