SLEEP 2011 Abstract Supplement

B. Clinical Sleep Science III. Sleep Disorders – Insomnia
0543
THE UTILITY OF POLYSOMNOGRAPHY IN PREDICTING
PERSISTENT INSOMNIA: A GENERAL POPULATION,
LONGITUDINAL STUDY
Fernandez-Mendoza J 1 , Vgontzas AN 1 , Singareddy R 1 , Liao D 2 ,
Calhoun S 1 , Kritikou I 1 , Basta M 1 , Karataraki M 1 , Bixler EO 1
1
Psychiatry, Penn State College of Medicine, Hershey, PA, USA,
2
Public Health Services, Penn State College of Medicine, Hershey, PA,
USA
Introduction: Chronic insomnia tends to be a persistent problem, with
only few experiencing full remission. None of the available populationbased,
longitudinal studies have examined the role of polysomnographic
(PSG) variables such as sleep apnea or sleep duration on the persistence
of insomnia. We hypothesized that objective short sleep duration will be
a strong predictor of persistent insomnia.
Methods: From a random, general population sample of 1741 adults of
the Penn State Cohort, 1395 were followed-up after 7.5 years. In this
study we included those with normal sleep at baseline and follow-up (n
= 590) and those who were insomniacs at baseline (n = 149) and developed
into persistent insomnia (n = 65), partially remitted insomnia (n =
47), or remitted insomnia (n = 37). Medical and sleep history and 8-hour
PSG were obtained at baseline, and sleep history also at follow-up. Multinomial
logistic regression models were adjusted for age, race, gender,
obesity, sleep apnea, physical health problems, mental health problems,
cigarettes, caffeine and alcohol consumption.
Results: Objective short sleep duration significantly increased the odds
of persistent insomnia as compared to normal sleep (OR=3.46) and to
remitted insomnia (OR=4.54) whereas sleep apnea did not predict either
the persistence or the remission of insomnia.
Conclusion: Objective short sleep duration is a strong predictor of persistent
insomnia. These data further support the validity and clinical utility
of objective short sleep duration as a novel marker of the severity of
insomnia.
0544
EEG SEGMENT DURATION CALCULATED BY ADAPTIVE
SEGMENTATION AS A MEASURE OF SLEEP STATE
STABILITY IN INSOMNIA
Turner J 1 , Bogan RK 1,3 , Amos Y 2
1
SleepMed of SC, SleepMed, Columbia, SC, USA, 2 WideMed,
WideMed, Herzliya, Israel, 3 School of Medicine, University of South
Carolina, Columbia, SC, USA
Introduction: Automated scoring of sleep can enhance analysis of the
EEG biological signal. This study utilizes adaptive segmentation to analyze
frequency segments across time looking at microstructure of sleep
and state analysis not restrained by 30 second epochs. Morpheus® uses
a multi-dimensional mathematical model of adaptive segments so that it
replicates what the human does in terms of looking at frequency and amplitude
characteristics. This study assesses signal processing outcomes
using adaptive segmentation at baseline comparing insomnia screen
fails(ISF) with normals(NL) and randomized insomnia(IN) patients.
Methods: A post-hoc analysis of three groups of adults is examined
based on automated analysis: 35 IN; 20 NL; and 38 ISF. HF mean segment
duration is reported. This represents baseline PSG pre-treatment
analysis. Advanced spectral parameters were analyzed in 2 hour time
intervals for each group. Means, standard deviations, and t-tests are reported.
Results: Means and standard deviations are measured as % of TIB.
Mean segment duration is: d(HF): hours 1-2: ISF=1.58 (0.38); IN=1.66
(0.37); NL=1.28 (0.16). Hours 3-4: ISF=1.28 (0.26); IN=1.23 (0.44);
NL=1.08 (0.19). Hours 5-6 ISF=1.18 (0.22); IN=1.22 (0.25); NL=1.07
(0.19). Hours 7-8 ISF=1.27 (0.22); IN=1.28 (0.25); NL=1.14 (0.19).
Results of t-tests of mean segment duration: d(HF) are significant p<
0.05 comparing NL to ISF/IN at each 2 hour segments and not significant
with ISF to IN at any time point studied.
Conclusion: Adaptive segmentation demonstrated an increase in high
frequency mean segment duration in insomnia and screen fail insomnia
patients compared to normals across each 2 hour interval. This suggests
in insomnia patients HF state is more persistent than in normals. These
findings support the premise of hyperarousal in insomnia.
0545
SLEEP EEG POWER SPECTRA TRANSITIONS DURING
SLOW WAVE SLEEP IN PRIMARY INSOMNIA
Bogan RK 1,3 , Turner J 1 , Amos Y 2
1
SleepMed of SC, SleepMed, Columbia, SC, USA, 2 WideMed,
WideMed, Hertzliya, Israel, 3 USC School of Medicine, Columbia, SC,
USA
Introduction: The pathophysiology of insomnia is not well understood.
There is no specific physiologic marker for this condition. Individuals
with insomnia are considered to be in a hyperaroused state during
sleep. This study assesses signal processing outcomes using adaptive
segmentation at baseline analyzing slow wave sleep (SWS) as a measure
of sleep homeostasis. We compared SWS dynamics across the
night in insomnia screen fails(ISF) with normals(NL) and randomized
insomnia(IN) patients. Morpheus® is a system that performs automated
analysis of sleep staging using a multidimensional mathematical analysis
of EEG applying adaptive segmentation and fuzzy logic with Markov
models enabling multiple spectral power EEG measurements.
Methods: A post-hoc analysis of spectral patterns of three groups of
adults is examined based on automated analysis: 35 IN; 20 NL; and 38
ISF. This represents baseline night analysis. Advanced spectral parameters
assessing slow wave sleep were analyzed for each group at 2 hour
intervals during the night and assessed the probability of transitioning
from slow wave sleep to a high frequency state.
Results: Means and standard deviations are measured as % of TIB.
For LF% hours 1-2: ISF=2.53(2.38); IN=2.53(2.65); NL=3.26(1.66).
Hours 3-4: ISF=3.78(2.52); IN=4.26(3.72); NL=2.97(2.18). Hours
5-6 ISF=2.09(1.62); IN=1.91(1.99); NL=1.23(1.39). Hours 7-8
ISF=0.92(0.94); IN=1.58(3.02); NL=0.49(0.65). T-tests of 2 hour intervals
of LF% comparing groups were significant p