SLEEP 2011 Abstract Supplement

B. Clinical Sleep Science XIV. Instrumentation and Methodology
0945
DURABILITY, SAFETY, EASE OF USE AND RELIABILITY OF
A TYPE 3 PORTABLE MONITOR AND A SHEET-STYLE TYPE
4 PORTABLE MONITOR
Kadotani H 1,2 , Nakayama-Ashida Y 2 , Nagai Y 1
1
Center for Genomic Medicine, Kyoto University Graduate School of
Medicine, Kyoto, Japan, 2 Horizontal Medical Research Organization,
Kyoto University Graduate School of Medicine, Kyoto, Japan
Introduction: To examine durability, safety, ease of use and reliability
to monitor obstructive sleep apnea syndrome with a type 3 (cardio-respiratory)
and a sheet-style type 4 portable monitors (PMs) in unattended
home settings.
Methods: A cross-sectional survey was conducted to male employees
of a wholesale company in Osaka, Japan (n=139, 44.4 ± 8.36 years).
Participants used either of the two PM on the second and third nights and
both of the PMs on the fourth nights. Ease of use was conducted by a
self-administered questionnaire. Results from the type 3 PM were manually
scored; while those form the type 4 PM were automatically scored.
Results: No safety problems were reported. A few repayments were
needed only for the type 3 PMs. The type 4 PM was more inconvenient
to bring home (type 3: 15.1%; type 4: 68.3%). The type 3 PM was more
inconvenient to use (type 3: 71.2%; type 4: 0.7%) and more uncomfortable
to use (type 3: 81.3%; type 4: 9.4%). Data from 107 and 133 out of
139 participants were comprehensible for both of two recorded nights
with the type 3 and the type 4 PMs, respectively. Blant- Altman plot and
scattered plot revealed high night-to-night reliability of these PMs
Conclusion: The type 3 and the type 4 PMs were durable, safe, easy
to use and reliable. Both the PMs can be used to monitor OSA in unattended
home settings.
Support (If Any): This work was supported by Special Coordination
Funds for Promoting Science and Technology, grants in aid from Ministry
of Health, Labor and Welfare of Japan, and research grants from
PRESTO JST, Suzuken Memorial Foundation, Takeda Science Foundation,
Mitsui Life Social Welfare Foundation, Chiyoda Kenko Kaihatsu
Jigyodan Foundation, and Health Science Center Foundation. We are
grateful to the participants, their family, and their company. Participants
of this study were employees of a company, which has financial interaction
with Kenzmedico.
0946
EVALUATING A NOVEL SLEEP DIAGNOSTIC SYSTEM
Verma N 2 , Zheng A 1 , Black J 3 , Ryder P 1
1
Huneo, LLC, Fremont, CA, USA, 2 Washington Township Center for
Sleep Disorders, Fremont, CA, USA, 3 Stanford University, Stanford,
CA, USA
Results: Visual comparison of the raw signals between PSG and the
new system revealed equivalent signal quality in all instances. AHI
scores (according to AASM guidelines) from the first 5 PSG studies
are tabulated below. (Results of the subsequent 7 PSGs are pending.)
Study/AHI (PSG)/AHI (new system) 1/34.7/33.4 2/2.0/2.0 3/4.8/3.8
4/53.4/49.4 5/112.5/103.1
Conclusion: We introduce a novel sleep data collection system with
real-time data viewing, transmitting and analysis capacity. The results
of a limited number of sleep studies from an on-going trial suggest that
data collected from this new system are equivalent to those from the
sleep-lab PSG.
0947
VALIDATION OF A NOVEL SLEEP EEG SLOW WAVE
AUTOMATED DETECTION ALGORITHM
Picot A, Van Cauter E, Chapotot F
University of Chicago, Chicago, IL, USA
Introduction: EEG slow waves (SW) are key components of deep
NREM sleep. A previous method to detect SW during sleep stages 3
and 4 has been proposed by Massimini et al. Here we propose a novel
algorithm to detect SW regardless of sleep/wake stages.
Methods: We first detected SW in filtered EEG signals (0.2-4Hz) with
criteria similar to previous methods using the PRANA biosignal processing
environment (PhiTools, France). Next, we introduced an additional
processing stage of the unfiltered signal to discard false positives.
We used a central EEG channel from a total of 191 PSG recordings
collected in young and middle-aged adults using portable or laboratory
systems. Sleep stages were visually score in 20-30s epochs as follows:
29357 wake, 11810 stage 1, 104357 stage 2, 13552 stage 3, 14909 stage
4 and 97164 REM. Non-parametric statistics were performed on the SW
temporal density to assess differences between algorithms, sleep stages
and recording equipments.
Results: Our algorithm detected a total of 230981 SW, as compared to
a total of 302000 using the Massimini method. Ninety-seven percent of
SW were detected in NREM sleep (2.79, 9.78 and 21.39 SW per min
for stages 2, 3 and 4, respectively), with a significant difference between
stages (chi2=602.46, p