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Assuming that
you want to use PSG assessment in an insomnia study, it becomes
important to determine what PSG features to report descriptively
or in response to an intervention. Looking at a graph such as
the one featured in Exercise B, one can determine the time in
bed, i.e. the total amount of time spent intending to plus actually
sleeping. Also one can determine the proportion of the time spent
actually sleeping. An important issue is the determination of
when sleep has begun. This varies somewhat across studies, but
sleep is frequently defined to begin with the first epoch (30
seconds) of non-REM stage 2 sleep. Several other variables can
be calculated as well from the PSG summary data that generally
have intuitively logical definitions:
- Sleep
Efficiency (SE): the proportion of time in bed actually asleep
(% or fraction)
- Fragmentation
Index: the number of sleep stage changes from deeper to lighter
stage or awake per hour
- Sleep
Onset Latency (SOL): the time to first sleep entry episode defined
as 30 seconds of stage 2 or sometimes 1 sleep.
- Wake After
Sleep Onset (WASO): the amount of awake time after first sleep
entry episode
- Arousal
Index (AI): the number of alpha wave or movement intrusions
into the EEG lasting 15 sec. - expressed per hour
As can be
seen, some of these physiologically measured sleep variables overlap
and therefore can be compared to some of the self-reported variables
(i.e. sleep onset latency, wake after sleep onset, sleep efficiency).
While the
manual scoring of brainwave amplitude and frequency tracings has
been done for many years, it is becoming increasingly common to
submit the EEG brainwave activity data to computer analysis and,
more specifically, spectral or period analysis to indicate more
refined and detailed sleeping brain physiology. Tools for spectral
analysis commonly include non-parametric methods such as the fast
Fourier transform (Cooley
and Tukey, 1965) and parametric methods such as autoregressive
(AR) modeling (see Drewes,
1999a). For the AR modeling, sleep EEG is divided into 5 frequency
bands: delta (0.5-3.5 Hz), theta (3.5-8 Hz), alpha (8-12 Hz),
sigma (12-14.5 Hz), and beta (14.5-25 Hz). For each band the power
spectrum is described and the squared amplitude (power) of the
different frequency components can be expressed as a function
of frequency, as shown below. Power spectral analysis of the EEG
has a potential advantage over traditional somnographic scoring
of the EEG in that fast changes in the spectral content of the
EEG not visible to the eye can be revealed.
| Figure
1.9.1: Power Spectral Analysis of an EEG
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| Reprinted
by permission: Drewes AM. Pain and sleep disturbances:
Clinical, experimental and methodological aspects with
special reference to the fibromyalgia syndrome and rheumatoid
arthritis. [doctoral thesis]. Aalborg, Denmark:
Aalborg University, 1999. |
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