Sleep Medicine 4 (2003) 207–212
www.elsevier.com/locate/sleep
Original article
An automatic ambulatory device for detection of AASM defined arousals
from sleep: the WP100
Giora Pillara, Amir Barb, Michal Betitob, Robert P. Schnallb, Itsik Dvirb,
Jacob Sheffyb, Peretz Lavie*,a,1
a
Sleep Laboratory, Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
b
Itamar Medical Ltd., Caesarea, Israel
Received 16 July 2002; received in revised form 1 October 2002; accepted 23 October 2002
Abstract
Objectives and background: Arousals from sleep are associated with increased sympathetic activation and therefore with peripheral
vasoconstriction. Sleep fragmentation in the form of multiple arousals is associated with daytime somnolence and cognitive impairment;
however, manual scoring of arousal is time consuming and problematic due to relatively high inter-scorer variability. We have recently
shown that automated analysis of in-lab recorded peripheral arterial tone (PAT) signal and the pulse rate derived from it can accurately assess
arousals from sleep as defined by the American Academy of Sleep Medicine (AASM). In the current study we sought to extend these findings
to the Watch_PAT100 (WP100), an ambulatory device measuring PAT, oximetry and actigraphy.
Methods: Sixty-eight subjects (61 patients referred to the sleep lab with suspected obstructive sleep apnea and seven healthy volunteers,
mean age 46.3 ^ 14.2 years) underwent a whole night polysomnography (PSG) with simultaneous recording of PAT signal by the
ambulatory WP100 device. The PSG recordings were blindly manually analyzed for arousals based on AASM criteria, while PAT was scored
automatically based on the algorithm developed previously.
Results: There was a significant correlation between AASM arousals derived from the PSG and PAT autonomic arousals derived from the
WP100 (R ¼ 0:87, P , 0:001), with a good agreement across a wide range of values. The sensitivity and specificity of PAT in detecting
patients with at least 20 arousals per hour of sleep were 0.80 and 0.79, respectively, with a receiver operating characteristic curve having an
area under the curve of 0.87.
Conclusions: We conclude that automatic analysis of peripheral arterial tonometry signal derived from the ambulatory device
Watch_PAT100 can accurately identify arousals from sleep in a simple and time saving fashion.
q 2003 Elsevier Science B.V. All rights reserved.
Keywords: Sleep; Autonomic nervous system; Arousals; Peripheral arterial tone; Ambulatory monitoring; Sympathetic activation
1. Introduction
Sleep fragmentation in patients with sleep apnea
syndrome can result in non-restorative sleep and consequent
daytime sleepiness and impairment of cognitive and
psychomotor performance [1 – 3]. Even normal subjects
become sleepier and their mood is impaired during the day
following experimental sleep fragmentation by brief
arousals [4]. Thus, the number of arousals is a useful
* Corresponding author. Giora Pillar, MD, Phd, Sleep Laboratory,
Gutwirth Building, Technion City, Haifa 32000, Israel. Fax: þ 972-48537404.
E-mail address: gpillar@tx.technion.ac.il (G. Pillar).
1
Amir Bar, Michal Betito, Robert Schnall and Jacob Sheffy are
employees of Itamar Medical and Giora Pillar and Peretz Lavie are
consultants for Itamar Medical Ltd.
marker of sleep quality, independent of traditional sleep
quality markers such as sleep latency, wake after sleep onset
and sleep efficiency. The currently recommended criteria
for scoring arousals consist of a notable EEG shift for at
least 3 s but no more than 15 s during all NREM stages of
sleep, assuming sleep is recorded prior to and following the
event for at least 10 s. Since EEG alpha waves or mixed
frequency waves are common during REM sleep, the
definition of arousal during REM sleep relies on a
combination of EEG defined arousal and increased EMG
or body movements [5]. These criteria are rather difficult to
determine, and a relatively large inter-scorer variability has
been reported in scoring arousals from sleep [6,7]. Thus, an
automatic and reliable method to detect arousals has been
sought [8]. We have recently reported that an automatic
analysis of peripheral arterial tone (PAT) signal recording –
1389-9457/03/$ - see front matter q 2003 Elsevier Science B.V. All rights reserved.
doi:10.1016/S1389-9457(02)00254-X
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G. Pillar et al. / Sleep Medicine 4 (2003) 207–212
a simple, reproducible and time saving procedure – can
accurately detect arousals from sleep [9]. In this previous
study, PAT signal has been recorded as an additional
channel in a standard polysomnography (PSG) set-up, with
sleep/wake scoring derived from the PSG. The fact that
standard PSG is a relatively cumbersome and expensive
procedure drives researchers to develop ambulatory
methods and devices. For the diagnosis of obstructive
sleep apnea (OSA), several devices have been produced
[10 –13], yet none have gained enough popularity to be
widely used for clinical purposes. For the detection of sleep
fragmentation, the pulse transit time (PTT) method has been
introduced. Pitson et al. reported a good correlation between
PTT and EEG frequency shifts in response to external
stimuli in normal subjects [14]. PTT could also, to some
extent, detect sleep disordered breathing events [15]. Argod
et al. found a reasonable agreement between standard
scoring of PTT in detecting non-apneic obstructive
respiratory events, but reported a very high inter-observer
variability in the scoring of both (30 – 37%) [16]. In the
current study, we sought to examine and validate the
accuracy of the recently developed ambulatory WP100
device (Watch_PAT100) in the detection of arousals from
sleep, as defined by the American Academy of Sleep
Medicine (AASM) [5].
2. Methods
2.1. Subjects
The study group consisted of 61 consecutive adult
patients referred to the Technion Sleep Disorders Center for
evaluation of presumed obstructive sleep apnea syndrome
(OSAS) and an additional seven young healthy volunteers,
recruited via advertisements in the Faculty of Medicine,
with no complaints of sleep disruption, daytime sleepiness
or snoring. The healthy volunteers were free of disease and
medications. The exclusion criteria for the suspected OSAS
patients were: permanent pacemaker, non-sinus cardiac
arrhythmias, peripheral vasculopathy or neuropathy, severe
lung disease, S/P bilateral cervical or thoracic sympathectomy, finger deformity that precludes adequate sensor
application, use of alpha-adrenergic receptor blockers (24
h washout period required), and alcohol or drug abuse
during the last 3 years. The study was approved by the
Rambam Medical Center Committee for Studies in Human
Subjects, and patients signed an informed consent prior to
participation.
Fifty-four of the participants were males and 14 were
females. A wide range of OSAS severities were represented
in the study group, with the respiratory disturbance index
(RDI) ranging from one to 118 events/phs. Twenty percent
of the subjects had hypertension and 4% had coronary artery
disease.
2.2. Protocol
All participants underwent a whole night PSG (Embla
system, Flaga HF, Iceland) with simultaneous recordings of
the WP100 device (Itamar Medical Ltd., Caesarea, Israel).
Prior to bedtime patients completed a sleep questionnaire
including physical data (e.g. weight and height), general
health condition and medical history, medication usage,
sleep habits, and the Epworth Sleepiness Scale (ESS) [17].
Lights-off was no later than midnight, and lights-on was at
06:00 h.
2.3. PSG
Overnight PSG was performed according to standard
laboratory protocol, using computerized PSG with the
following channels: two EEG (C3-A2 and O2-A1), EOG,
submental EMG, arterial oxygen saturation, nasal – oral
airflow (thermistors and nasal pressure), EKG, chest and
abdominal wall motion (piezo electrodes), bilateral anterior
tibialis EMG, and body position. Sleep was staged
according to standard criteria [18]. Arousals were defined
according to the AASM guidelines [5]. An EEG frequency
shift of at least 3 s but no more than 15 s during non-REM
sleep was scored as an arousal; during REM sleep an
increase in EMG was required as well, and in both cases at
least 10 s of sleep prior to and following the event was
required. The arousal index (ARI) was calculated by
dividing the total number of arousals by the number of
hours of sleep, and has been termed PSG-ARI. Respiratory
events were scored according to the AASM guidelines for
measurement in clinical research [19]. The RDI was
calculated as the number of apneas plus hypopneas divided
by the number of hours of sleep.
2.4. The Watch_PAT 100 device
The WP100 device is an ambulatory system comprised
of a battery powered consol unit, mounted just above the
wrist, with embedded actigraph and oximetry. It has two
finger mounted probes: pulse oximetry and PAT. This
system has been described in detail elsewhere [20]. In brief,
it records three signals (actigraphy, PAT, oximetry) with a
sampling rate of 100 Hz, and stores the data throughout the
sleep study on a removable flash disc. A fourth channel,
pulse rate, is derived from the PAT signal. Sleep/wake is
determined by the actigraphy, and the arousals are scored
automatically using an improvement of an algorithm,
previously described [9]. The algorithm scores an arousal
if one of the following conditions is fulfilled within an epoch
scored as sleep:
1. An association between two events – an attenuation of
the PAT signal amplitude (vasoconstriction) below a
threshold and an increase in pulse rate above a threshold.
The thresholds for both events are adaptive and set in a
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first pass learning process of the patient’s recorded data
file according to the ratio between the number of these
events.
2. An association between PAT signal amplitude attenuation of more than 40% and short movements of the
patient, detected by the energy of the actigraphic signal.
The total number of arousals scored was divided by the
number of hours of sleep (as assessed by the WP100), and
termed PAT-AAI.
2.5. Data and statistical analysis
Different personnel separately and blindly performed the
PSG manual scoring and the PAT automatic scoring. The
primary outcome measures in this study were the AASMbased arousal indices (PSG-ARI), which were considered
the gold standard, and the PAT-based autonomic arousal
indices (PAT-AAI), which were the evaluated measures.
The agreement between these two measures was assessed in
three ways. First, correlation analysis was performed.
Second, Bland – Altman analysis was carried out to assess
potential range-dependant agreement. Finally, in order to
evaluate the efficacy of the PAT automatic analysis as a
potential tool for diagnosing a sleep arousal disorder, a
threshold of AAI ¼ 20 [21] for abnormality was defined, a
receiver operating characteristic (ROC) curve plotted, and
its area under the curve (AUC) calculated. This curve joins
points on an X_Y plain (x ¼ 1 2 specificity, y ¼ sensitivity)
for all possible values of the PAT-AAI thresholds. The AUC
is considered a measure of the overall efficacy of the score;
an AUC value of 0.5 indicates a non-significant score for
separating normal from abnormal patients and a value close
to 1.0 indicates a very efficient score.
In addition, the correlation of both PSG-ARI and PATAAI with the Epworth Sleepiness Score was determined to
assess their ability to predict subjective sleepiness.
Table 1
Characteristics of the study group
RDI
n
Age (years)
BMI
ESS
,10
10–20
21–40
.40
Overall
14
35 ^ 16
25 ^ 4
6^6
14
44 ^ 14
27 ^ 4
9^5
15
50 ^ 9
27 ^ 5
10 ^ 5
25
52 ^ 12
32 ^ 6
12 ^ 6
68
46 ^ 14
28 ^ 6
9.5 ^ 6
RDI, respiratory disturbance index (events/hour of sleep); n, number of
subjects; BMI, body mass index (kg/m2); ESS, Epworth Sleepiness Scale
(0–24). Data are presented as the mean ^ SD.
good agreement between the two parameters across a
wide range of arousal indices.
Fig. 3 shows the ROC curve for PAT identification of
patients with pathological AAI, as defined by a threshold
of 20 arousals per hour of sleep [21] when taking the
AASM-based scoring as ‘gold standard’, with an AUC of
0.87. The sensitivity and specificity of the WP100 in
detecting patients with at least 20 arousals/phs were 0.80
and 0.79, respectively.
Finally, although statistically significant, there were only
poor correlations between the arousal indices (either by
standard criteria or by PAT) and subjective sleepiness as
assessed by ESS (R ¼ 0:33 and R ¼ 0:34, respectively,
P , 0:001 for both).
4. Discussion
This study shows that the standard AASM-based ‘EEG
arousals’ can be accurately assessed by measuring ‘autonomic arousals’ using the WP100 ambulatory device. This
is consistent with the observation that arousals are
3. Results
Characteristics of the study population are presented in
Table 1. The average age and BMI were 46 ^ 14 years and
28 ^ 6 kg/m2, respectively. The average RDI for the whole
group was 34 ^ 26 events/h. As can be seen, there were
similar numbers of subjects in the various apnea severity
ranges. The average ESS score for the whole group was
9.5 ^ 6, with the score tending to be higher as the severity
of apnea increased.
Fig. 1 displays a scatter graph of the PSG-ARI (gold
standard) vs. the PAT-AAI values for the whole study
population with the calculated correlation coefficient.
There was a good and statistically significant correlation
between the two parameters (R ¼ 0:87, P , 0:0001).
Fig. 2 displays the Bland –Altman plot for PSG-ARI
and the PAT-AAI for the study population. There was a
Fig. 1. PSG-ARI vs. PAT-AAI. A very high and statistically significant
correlation (R ¼ 0:87, P , 0:0001) was found between the PAT-AAI
(PAT-based autonomic arousal index) and the PSG-ARI (arousal index
derived from the PSG based on AASM criteria).
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G. Pillar et al. / Sleep Medicine 4 (2003) 207–212
Fig. 2. Bland–Altman presentation of ASDA-ARI vs. PAT-ARI. Across a
wide range of arousals frequencies, there was a good agreement between
PAT-ARI (PAT-based arousal index) and the PSG-ARI (arousal index
based on AASM criteria).
associated with sympathetic activation, and can be accurately measured by an in-lab PAT channel added to a
standard PSG [9].
Until now, ambulatory sleep monitoring equipment has
been focused on measuring only the sleep/wake state
(actigraphy) [22], sleep apnea indices (primarily oximetry,
but also other methods) [11,13,23,24], or multi-channel
measurement (home PSG) [10,12,25]. In the current study
we have concentrated on measuring autonomic arousals
from sleep, and have used a relatively novel channel (PAT
signal) monitored by an ambulatory device (WP100).
Brief arousals from sleep may impair cognitive and
psychomotor performance, particularly in sleep-related
breathing disorders [1,4]. Thus, quantification of arousals
may have an important clinical role in patient assessment.
However, EEG arousals, as currently defined, have several
disadvantages. EEG recordings must be done either in the
lab or by a relatively cumbersome ambulatory monitoring
system, and scores are unreliable, primarily due to a
considerable inter-scorer variability. Drinnan et al., using
experts from 14 sleep laboratories to evaluate the reprodu-
Fig. 3. ROC curve for identifying pathological arousals from sleep
(threshold: 20 arousals per hour of sleep) based on PAT vs. standard
criteria. The AUC is 0.87, yielding potentially high sensitivity and
specificity in diagnosing pathological arousal frequency by PAT, when the
gold standard is the AASM criteria based on PSG.
cibility of EEG arousal scores as defined by the AASM,
report a rather large disagreement and considerable
variability in the scoring of these events [7]. In another
study Loredo et al., evaluating various types of arousals,
showed good inter-scorer reproducibility in scoring Periodic
Leg Movements (PLMs) or respiratory events accompanied
by arousals characterized by increased EMG. Poor reproducibility was demonstrated in the scoring of ‘classic’ AASM
defined arousals [6]. Thus, an automated method of
detecting sleep fragmentation, which by definition does
not present an inter-scorer variability, seems warranted.
Two additional methods based on sympathetic activation
have been previously attempted. The PTT has initially
demonstrated some promising results, and also has the
advantage of potential use in the home environment [26].
However, it has not been compared in large-scale studies to
standard measures, and has been shown to have the
disadvantage of high inter-observer variability [16]. Davies
et al. used arousal combined with inspiratory blood pressure
changes as a potential marker for disturbed sleep and
disordered breathing in sleep [27]. Although blood pressure
can be measured non-invasively by an ambulatory device,
and the blood pressure profile seems to be useful in
identifying patients with OSA, this method has not been
further investigated – perhaps because of the impracticality
of continuous blood pressure measurements throughout the
night. The monitoring of PAT, which we have used in the
current study, has several advantages. First, the very simple
device is located only on the hand and requires minimal
technical intervention in patient preparations and recording.
The applied pressure to the finger (approximately 50
mmHg) did not cause any adverse effect or discomfort to
the subjects. Second, the analysis of the PAT data is
automatic (computerized), which makes it reproducible,
objective and time saving. In the current study we have not
quantified time consumption by each method of arousal
assessment, but we roughly estimate that AASM-based PSG
arousal scoring took some 40 –60 min per record, while the
automatic PAT-based arousal count, with all overheads
(such as downloading the patient file to a work station),
takes less than 5 min per record. Third, the incorporation of
actigraphy enables this automatic analysis to be applied
during sleep periods only.
Although the aim of our study was to assess the ability of
the ambulatory device WP100 to detect arousals from sleep,
in the present study we have used this device in the sleep lab
in order to compare the measured arousals to standard
measurements by full PSG (AASM criteria). All three
methods of comparisons between the AASM and PATbased AAI (correlation analysis, Bland – Altman analysis
and ROC) revealed reasonably good agreement, suggesting
that the PAT-based algorithm may accurately reflect sleep
fragmentation. As discussed elsewhere [9], PAT-based
arousals are recognized based on analysis of two important
sympathetic-related parameters: digital vasoconstriction
and pulse rate changes, both measured by the finger
G. Pillar et al. / Sleep Medicine 4 (2003) 207–212
probe. It has been shown that arousals from sleep are
associated with increased sympathetic activation [28 – 32].
The WP100e algorithm, which detects autonomic arousals,
is activated only in epochs of sleep, as determined by the
actigraphic part of the WP100. In this study, totally
independent analysis of the PSG and WP100 (although
simultaneously recorded) revealed a reasonably good match
regarding sleep fragmentation. Thus, we believe that this
device has the ability to serve as a useful ambulatory device
for detecting sleep fragmentation in patients’ homes,
although home studies have not been performed in the
present study. It should be stressed, however, that the
criteria for detecting EEG arousals were rather arbitrarily
determined by a task force of the American Sleep Disorders
Association (ASDA) [5]. Much more data are required to
study the best criteria of sleep fragmentation in predicting
clinical outcomes. In fact, both the EEG and the autonomic
arousals in our study were poorly (although statistically
significantly) correlated with the ESS, probably indicating
either poor subjective assessment by patients of their
sleepiness level, or an objective limitation of the EEG
arousal index and AAI prediction. This is not surprising, as
previous studies have also failed to show good correlation
between arousals from sleep and daytime sleepiness [15,26,
33]. As this study showed good agreement between the PAT
defined AAI and the EEG defined arousals index, the poor
correlation between the PAT-AAI and subjective daytime
sleepiness assessment was also expected. When originally
introduced, the ESS demonstrated good correlation with
objective sleepiness measures (Multiple Sleep Latency Test,
MSLT) [17], which later studies failed to replicate [34].
Also, it should be kept in mind that the sleep study, as well
as MSLT, represents an acute state reflecting a specific point
in time, while the ESS represents a trait measure correct for
the recent period. Thus, the ability of the PAT to predict
daytime sleepiness needs further evaluation, probably with
more objective modalities such as MSLT.
There are several limitations in this study. Although the
purpose of the study was to evaluate an ambulatory device,
the studies took place in the sleep lab because we wanted to
assess at the outset the accuracy of the device in detecting
arousals. Obviously this device will have to be studied in the
home environment during a second stage. Second, we aimed
at detecting sleep fragmentation but chose to compare
autonomic arousals to the commonly detected EEG
arousals. Per definition, we ignored EEG shifts that were
shorter than 3 s, even if there was a clear prior flow
limitation. It is not unlikely that we limit the PAT’s
usefulness, and the potential augmentation of its clinical
use, by comparing it only to AASM defined arousals when it
may be sensitive to shorter arousals. Further studies of this
methodology are needed to find an optimal marker of sleep
fragmentation that can predict clinical outcome. Third, the
population studied consisted primarily of patients with
snoring/sleep apnea syndrome, and a few healthy volunteers. One could argue that in other populations (such as
211
insomniacs) arousals from sleep might be associated with
different patterns of autonomic activation; expanding the
population of the study will add more information regarding
autonomic arousals.
Despite these limitations, we believe that this study
supports the concept that an ambulatory device measuring
PAT at the finger can accurately assess sleep fragmentation.
Autonomic arousals can be assessed in an objective,
reproducible and time saving procedure using a computerized automated algorithm.
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