Jaw - Actigraphy - Polysomnography - Mandible
INTRODUCTION
Epidemiological studies carried out in the past decade have shown that obstructive
sleep apnea (OSA) is a highly prevalent medical condition in the general population.
It implies a range of clinical presentations with an ever-growing list of known adverse
health consequences[1]. The diagnosis and severity of OSA are determined by the apnea-hypopnea index (AHI),
which itself requires an accurate and objective assessment of both sleep time and
respiratory events. Full in-lab polysomnography (PSG) is considered as the gold standard
in the diagnosis of OSA, however this procedure can be costly and time-consuming limiting
thus its scope. Consequently, alternative approaches based on portable devices used
for home sleep testing (HST) were developed from early on[2].
Whilst HST devices make use of different combinations of PSG respiratory sensors to
identify respiratory events, they all lack the objective EEG measurement of the total
sleep time (TST). Therefore, the International Classification of Sleep Disorders (ICSD)
recommends that, in those conditions, the total recording time be used instead of
the total sleep time in calculating respiratory disturbance index (RDI)[3]. This change of a variable, obviously, impacts both the diagnostic outcome and disease
severity stratification[4]. To overcome this problem, many HST devices are using either a separate or an integrated
actigraphy device (ACTG) as a surrogate for the EEG measurement of the TST. Although
these devices improved the overall sensitivity and specificity of HST devices, they
turn out to be less reliable in the presence of comorbidities, a situation frequently
reported in patients who require a sleep study. The use of ACTG technology in association
with HST devices is therefore considered as “conditional” rather than “standard” recommendation[5],[6]. Otherwise, a growing number of HST devices resorts to technologies that do not
make use of the same recommended variables for respiratory events as used in standard
PSG, but instead rely on surrogate parameters. In such an approach, the number of
channels used on a given device becomes less relevant than the sensitivity and specificity
of the device and the clinical outcomes that it can achieve[7].
One of the most promising surrogate used in HST is the analysis of the sagittal mandibular
movement (MM) using a high-resolution magnetometer named JAWAC. Such analysis is able
not only to recognize but also to differentiate between different sleep related respiratory
events[8]. The algorithm used in the automatic analysis software of MM compared to PSG, proved
to be a reliable alternative to the latter[9]. Moreover, as the sagittal mandibular movements reflected different behaviors associated,
either to wakefulness (speaking, swallowing, eating, drinking, tonic support…), or
to sleep (quiescence), we implemented another algorithm in order to detect sleep and
wake epochs. This complementary algorithm provided a good estimation of the sleep
and wake states[10]. The use of a single sensor to measure both the relevant respiratory and sleep parameters
in a reliable way, offers a definite advantage that requires further validations.
In a previous study conducted on healthy adults, we showed that MM was comparable
to standard PSG and superior to ACTG in differentiating sleep and wake states[11]. However, the presence of sleep disturbances or comorbidities may interfere with
the ability of a device to measure sleep. So, we designed the present study in order
to assess the accuracy of predicting sleep and wake states by the analysis of MMs,
confronted to synchronized analyses of ACTG and PSG, the latter one considered as
the gold standard, in a cohort of patients suffering from a sleep disorder.
Participants
The study was conducted at the Sleep Center of the University Hospital of Liege, Belgium.
In accordance with the Helsinki declaration on human experimentation[12], every participant read and signed an informed consent in which the aims of the
study were also explained. They were selected from a group of adult patients who had
been referred to the sleep center for investigation of sleep complaints. All patients
were 18 years old or above. Their medical history, medications and demographic data
were collected. The size of the cohort was limited to the first 100 patients in whom
the simultaneous recordings of the devices were completed without technical defects.
Measurements
Polysomnography
PSGs were carried out using EMBLA N7000 systems equipped with the Somnologica software.
The PSG montage included three EEG channels, left and right EOG, chin EMG, bilateral
tibialis anterior EMG, EKG, nasal cannula/pressure transducer, chest, and abdominal
inductance plethysmography belts, fingertip pulse oximetry, snoring sensor, body position
sensor, and light sensor. The manual scoring was done according to AASM scoring rules
and was realized by qualified technologists blinded to the results of the other devices[2]. PSG was named hereafter as the gold standard for wake and sleep identification
as well as for the diagnosis of sleep disorders.
Tested device: actigraphy (ACTG)
Actiwatch monitor (Actiwatch 2; Philips - Respironics, Murrysville, PA, USA) attached
to the nondominant wrist was used for that purpose. Data were collected in 30-seconds
epochs and analyzed thereafter by Philips ActiWare software version 6.0.1. The “default”
settings provided by the manufacturer were selected for automatic analysis[13].
Tested device: JAWAC
The JAWAC (Nomics - Liege, Belgium)[14] is a device validated in the diagnosis of sleep breathing disorders through an analysis
of mandibular movements. It employs a noninvasive motion sensor, based on the principle
of electromagnetic self-induction. The output voltage at the receiver coil is a monotonic
cubic function of the distance between the transmitter and the receiver coils. When
the two coils are placed parallel to each other on the median-line of forehead and
chin, the distance between them, which represents the sagittal MM, can be calculated
from the properties of the received signal. The output was amplified, digitalized
at a rate of 10Hz and made available online with the PSG channels. The data were also
stored for subsequent retrieval and analysis. A first software based on MM analysis
to detect and classify the ventilatory effort has been developed and validated[14]. Furthermore, a second validated software, using a wavelet-based complexity measure
of the MM signal, was proposed to recognize sleep and wake states[10].
Procedures
To ensure a reliable temporal synchronization between the three devices, we used the
“Network Time Protocol”. Before each sleep study, the computer from each device was
connected to the Internet and its clock synchronized manually with the Internet timeserver.
Several units of each device were available for randomly use. Patients were admitted
to the sleep laboratory between 14:00 and 17:00 hours. They were equipped early with
ACTG and JAWAC sensors while those of PSG were installed later in the evening. Each
patient freely chose the time devoted to sleep, in accordance with his or her bed
and wake habits. The data from each device was stored for subsequent retrieval and
analysis. At the end of each sleep study, the sleep technologist made sure that the
three devices had remained synchronized, defined as showing no more than 30 seconds
of discrepancy.
Data analysis
For methodological reasons, the duration of the recording was different with the three
devices. Consequently, as they were all synchronized, we selected the period from
“lights out” to “lights on” identified by the PSG sensors, as the time base for analysis
for all three devices (gold standard PSG, tested device ACTG, and tested device JAWAC).
Qualified technologists scored the data from the PSG recordings manually and according
to the AASM scoring rules. Automatic analyses were used in order to get ACTG and JAWAC
data. The results of the scorings were available in 30sec. epochs. The PSG epochs
were reduced to a binary form (S for any sleep stage and W for wakefulness), while
those of ACTG and JAWAC were labeled directly ‘sleep (S)’ or ‘wake (W)’ by automatic
analyses.
For each device, five derived sleep parameters were calculated using the same definitions.
These included three AASM recommended parameters: 1) the total sleep time (TST) defined
as the duration of all epochs labeled as sleep; 2) the sleep onset latency (SOL) measured
as the time from light-off to the first epoch of sleep; and 3) the wake after sleep
onset (WASO), which is the time scored as wake from first sleep epoch to light-on.
Two additional parameters were added: 1) the wake during sleep period (WDSP), calculated
as the time of wake between the first and the last epoch of sleep and 2) the latency
to arising (LTA) measured as the elapsed time from last sleep epoch to light-on[15].
Statistical analysis
We assessed the outcomes of the three devices both on a pooled-epoch and on a per-subject
basis. The objectives of the statistical analysis were threefold: 1) to compare epoch
by epoch the respective abilities of JAWAC and ACTG to differentiate sleep from wake
states; 2) to compare their estimates of sleep parameters; and 3) to explore the differences
between them.
In the epoch by epoch comparison, we combined all scored epochs from all subjects
on a pooled-epoch basis and for each device. A three-way presentation of the results
was used in accordance with the recommendations of the statistical guidance of the
Food and Drug Administration (FDA)[16].
The classification of the epochs by the PSG was used as reference. According to Tyron’s
method[17], sensitivity, specificity and accuracy were used to compute the percentage of matching
epochs between each tested device and PSG. Sensitivity is the measure of the correctly
identified sleep epochs. It is calculated by dividing the number of epochs correctly
recognized by the tested device as sleep by the total number of PSG-identified sleep
epochs. Specificity is the proportion of correctly identified wake epochs and is calculated
by dividing the number of epochs the device correctly identified as wake by the total
number of PSG-identified wake epochs. Accuracy is the overall agreement between PSG
and the device. Accuracy is determined by dividing the cumulative number of correctly
identified sleep and wake epochs by the total number of epochs in the recording period.
Wake and sleep epoch agreements were analyzed for each device against PSG using the
Cohen’s Kappa correlation, which determines the amount of agreement that can be expected
by chance. This statistic ranges from 1, which demonstrates perfect agreement, to
0 which demonstrates agreement based on chance alone, and to -1 which demonstrates
complete disagreement.
To investigate the differences between ACTG and JAWAC, we analyzed their agreements
and disagreements. First, three agreement levels between ACTG and JAWAC were calculated:
a) the overall agreement, is the percentage of the total number of epochs labeled
identically by the two tested devices; b) the agreement in ‘sleep epochs’ is the percentage
of identical labeling by the two tested devices in the epochs scored by the PSG as
sleep; and c) the agreement in ‘wake epochs’ is the percentage of identical labeling
by the two tested devices in the epochs scored by the PSG as wake. Second, in epochs
where the two tested devices disagreed, we used the discrepant resolution test considering
PSG as resolver to determine the ‘right’ device.
In the comparison between the estimates of sleep parameters, we proceeded to a per-subject
analysis. The means and standard deviations of each sleep parameter were calculated
for PSG, ACTG and JAWAC. The Pearson correlation coefficient, with 95% confidence
interval was reported for the two devices against the PSG gold standard values. One-way
ANOVA tests were used to verify if the ACTG and JAWAC estimates varied from the PSG
measurement and Pairwise t-tests with adjusted p-values (using Holm-Bonferroni correction) were used to determine the significance
of any difference between ACTG and JAWAC with the null hypothesis (the two devices
provide equivalent performance). All statistically significant conclusions are made
at an α=0.05 level.
To illustrate the data, we calculated for each subject the difference between the
PSG measurements and the ACTG and JAWAC estimates. The mean differences (bias), the
standard deviation of the differences and the limits of agreement were reported. A
positive bias indicates an overestimation, and a negative bias indicates an underestimation
relative to the PSG analysis, by the ACTG or JAWAC. For each sleep parameter, we displayed
the Bland and Altman plots. Since PSG is the gold standard, we considered for each
subject the PSG results rather than the mean of two methods to be plotted against
the difference between PSG measurements and ACTG and JAWAC estimates[18].
RESULTS
One hundred seven sleep recordings were needed to obtain 100 simultaneous recordings
unaffected by technical faults: 3 recordings were excluded for lack of synchronization,
1 for ACTG software problem, and 3 for JAWAC signal loss.
The demographic characteristics and sleep variables for the three devices are presented
in [Table 1]. PSG was considered as normal in 6 patients (normal distribution and proportion
of sleep stages; IAH <5; PLM <15/h). 42 patients were diagnosed with severe sleep
apnea syndrome (IAH >30), 31 with moderate SAS (15<IAH<30) and 19 with mild SAS (5<IAH<15).
Periodic limb movement (PLM >15/h) was present in 70 patients.
Table 1
Demographic and sleep parameters of the participants (n=100).
|
Variables
|
Value (mean ± SD)
|
Range
|
|
Gender (male/female)
|
59 : 41
|
|
|
Age (years)
|
47.3 ± 14.4
|
19 - 87
|
|
Body mass index (kg/m2)
|
30.1 ± 5.9
|
17.5 - 49.1
|
|
Epworth sleepiness scale
|
11.7 ± 4.7
|
2 - 21
|
|
Hospital anxiety and depression scale (A-subscale)
|
9.1 ± 4.4
|
1 - 20
|
|
Hospital anxiety and depression scale (D-subscale)
|
7.1 ± 4
|
0 - 19
|
|
AHI (episodes/h)
|
31.3 ± 22.2
|
0.6 - 108.5
|
|
PLMI (episodes/h)
|
14.4 ± 9.8
|
1.1 - 57
|
|
Time in bed (min)
|
532.2 ± 91.7
|
210.5 - 771.5
|
|
Total sleep time (min), measured by PSG
|
405.6 ± 84.9
|
128.5 - 602.5
|
|
Total sleep time (min), estimated by ACTG
|
461.6 ± 95.2
|
135 - 648.5
|
|
Total sleep time (min), estimated by JAWAC
|
428.6 ± 97.9
|
4 - 687
|
|
Sleep efficiency (%), measured by PSG
|
60.3 ± 12.8
|
22.1 - 89.7
|
|
Sleep efficiency (%), estimated by ACTG
|
53.4 ± 12.2
|
0.5 - 84.5
|
|
Sleep efficiency (%), estimated by JAWAC
|
52.5 ± 13.1
|
18.9 - 85.4
|
Performance of the sleep /wake classification
In [Table 2], a three-way presentation compares the sleep/wake classifications according to each
device and the different combinations of labeling between ACTG and JAWAC for the overall
epochs (106,456) and also for epochs scored by the PSG as sleep (81,169) or wake (25,287).
Table 2
A three-way presentation of sleep/wake classification comparing the ACTG, the JAWAC,
and the PSG.
|
ACTG
|
JAWAC
|
Number of Epochs
|
PSG
|
|
Sleep
|
Wake
|
|
Sleep
|
Sleep
|
79,956
|
72,249
|
7,707
|
|
Wake
|
Sleep
|
5,784
|
2,249
|
3,535
|
|
Sleep
|
Wake
|
13,380
|
5,885
|
7,495
|
|
Wake
|
Wake
|
7,336
|
786
|
6,550
|
|
|
106,456
|
81,169
|
25,287
|
The performance of ACTG and JAWAC compared to PSG are presented in [Table 3]. Both devices showed an identically high level of accuracy (82.86% for ACTG and
83.17% for JAWAC). The high sensitivity levels (96.26 for ACTG and 91.78 for the JAWAC)
confirm their excellent ability to identify epochs of “sleep”. Whereas a higher specificity
of JAWAC (55.54) compared with ACTG (39.88) indicates its greater efficiency in identifying
epochs of “wake”. The JAWAC’s Cohen’s Kappa coefficient (0.50) even though moderate,
was slightly higher than ACTG (0.43).
Table 3
Comparative performance on a pooled-epoch basis.
|
ACTG
|
JAWAC
|
|
Fractions
|
%
|
Fractions
|
%
|
|
Accuracy
|
(72,249+5,885+3,535+6,550)/106,456
|
82.86
|
(72,249+2,249+7,495+6,550)/106,456
|
83.17
|
|
Sensitivity
|
(72,249+5,885)/81,169
|
96.26
|
(72,249+2,249)/81,169
|
91.78
|
|
Specificity
|
(3,535+6,550)/25,287
|
39.88
|
(7,495+6,550)/25,287
|
55.54
|
|
Cohen’s Kappa
|
0.43
|
|
0.50
|
|
Sleep parameters concordance
The sleep parameters calculated by each of the three devices are shown in [Table 4]. All ACTG estimates differed significantly from their corresponding PSG measures.
TST was largely overestimated by ACTG, while the other parameters related to ‘wake’
were all underestimated.
Table 4
Sleep parameters on a per-subject basis as calculated by the three devices.
|
PSG
|
ACTG
|
JAWAC
|
|
Measures
|
Estimates
|
Correlation coefficient
(95% confidence interval)
|
Estimates
|
Correlation coefficient
(95% confidence interval)
|
|
TST
|
405.6 ± 84.9
|
461.6 ± 95.2ab
|
0.64* (0.54 , 0.88)
|
428.6 ± 97.9b
|
0.73* (0.68 , 0.99)
|
|
SOL
|
32.4 ± 27.8
|
3.3 ± 3ab
|
0.11 (-0.01 , 0.33)
|
32.8 ± 51.8b
|
0.29* (0.17 , 0.88)
|
|
WASO
|
94.3 ± 68.0
|
62.2 ± 35.7a
|
0.71* (0.29 , 0.44)
|
70.9 ± 61.8a
|
0.54* (0.34 , 0.64)
|
|
WDSP
|
83.2 ± 68.9
|
60.4 ± 35.1a
|
0.66* (0.26 , 0.41)
|
61.9 ± 59.4a
|
0.54* (0.32 , 0.61)
|
|
LTA
|
11.1 ± 16.7
|
1.8 ± 2.2ab
|
0.13 (-0.09 , 0.04)
|
9 ± 12.1b
|
0.69* (0.39 , 0.60)
|
Notes: Statistically significant inter-instrument results are marked in the table:
*p<0.05; aSignificant difference from PSG measures; bSignificant difference between ACTG and JAWAC estimates.
The Pearson correlation coefficient between ACTG and PSG showed a good and significant
correlation for TST, WASO, and WDSP and poor or non-significant correlation for SOL
and LTA.
The JAWAC’s estimates of TST and SOL were not significantly different from the PSG
measurements, while WASO/wake time was underestimated and significantly different.
Interestingly, a differentiation of WASO into its two components, WDSP and LTA, showed
that this difference is due to the WDSP, which is underestimated rather than to the
LTA, which does not show a significant difference. The correlation coefficient for
the JAWAC estimates showed a significant correlation for all the parameters with a
good correlation for TST and LTA and a modest one for SOL, WASO, and WDSP.
Bias and precision statistics
The Bland and Altman statistics for the sleep parameters are described in [Table 5] and the plots are shown in [Figure 1]. According to these results, the directions of the biases were the same for both
devices: an overestimation of TST by the 2 tested devices but the overestimation with
the JAWAC was less than half the one for the ACTG. Moreover, the JAWAC expressed a
rather adequate estimation of SOL. The other wake parameters were underestimated by
the two tested devices but far less for the LTA, with the JAWAC. The biases with JAWAC
were closer to zero and with more tightened limits of agreement for all sleep parameters
and showed a greater degree of constancy with regard to TST, SOL, and LTA. ACTG on
the other hand showed a constant bias only with regard to TST whereas they tended
to diverge farther as the values for PSG increased.
Figure 1 Bland-Altman plots for TST (total sleep time), SOL (sleep onset latency), WASO (wake
after sleep onset), WDSP (wake during sleep period), and LTA (latency time to arising).Notes:
( - ) Mean difference; X - axis reflects the mean of the PSG and the device.( ----
) 2 Standard deviations; Y - axis is denote the difference between PSG and the device.
Table 5
Bland and Altman plot statistics.
|
ACTG
|
JAWAC
|
|
Mean ± SD
|
ULOA; LLOA
|
Mean ± SD
|
ULOA; LLOA
|
|
TST
|
56.0 ± 77.4
|
207.6 ; -95.7
|
23.0 ± 68.3
|
156.9 ; -111.0
|
|
SOL
|
-29.2 ± 27.6
|
25.0 ; -83.3
|
0.4 ± 51.4
|
101.1 ; -100.2
|
|
WASO
|
-32.0 ± 49.4
|
64.8 ; -128.9
|
-23.4 ± 62.3
|
98.6 ; -145.4
|
|
WDSP
|
-22.8 ± 52.7
|
80.5 ; -126.1
|
-21.3 ± 61.9
|
100.1 ; -142.7
|
|
LTA
|
-9.3 ± 16.5
|
23.1 ; -41.6
|
-2.1 ± 12.1
|
21.6 ; -25.8
|
Notes: Analyses were conducted on the difference between PSG measurements and ACTG
and JAWAC estimates. Negative value of mean indicate underestimation; positive value
of mean indicate overestimation; SD = Standard deviation; ULOA; LLOA = Upper and lower
limits of agreement.
Exploring the differences between ACTG and JAWAC
[Table 6] presents on a pooled-epoch basis, the analysis of agreements and disagreements between
ACTG and JAWAC in classifying epochs into sleep and wake. These results showed a good
level of agreement (0.82) between the two devices, and where they agreed, their degree
of convergence with the reference PSG scoring was excellent (0.90). We used the discrepant
analysis of disagreement with PSG as a resolver to analyze the disagreements between
ACTG and JAWAC[16]. This showed that both devices overall produced the same level of agreement with
PSG (0.49 for ACTG and 0.51 for JAWAC). ACTG was more accurate in correctly identifying
the sleep epochs (0.72). Conversely, JAWAC was more accurate in correctly identifying
wake epochs (0.68).
Table 6
Analysis of agreement and disagreement between ACTG and JAWAC in a pooled-epoch basis.
|
Fractions
|
Percentages
|
|
Agreement analysis
|
|
|
Overall agreement
|
(79,956+7,336)/106,456
|
82
|
|
ACTG & JAWAC agree and are both correct
|
(72,249+6,550)/(79,956+7,336)
|
90
|
|
ACTG & JAWAC agree and are both wrong
|
(7,707+786)/( 79,956+7,336)
|
10
|
|
Overall disagreement
|
(5,784+13,380)/106,456
|
18
|
|
Discrepant analysis of disagreement - PSG as resolver
|
|
|
Overall epochs with disagreement
|
|
|
|
ACTG agree with PSG
|
(5,885+3,535)/( 5,784+13,380)
|
49
|
|
JAWAC agree with PSG
|
(2,249+7,495)/(5,784+13,380)
|
51
|
|
Sleep’s epochs
|
|
|
|
ACTG agree with PSG
|
5,885/(2,249+5,885)
|
72
|
|
JAWAC agree with PSG
|
2,249/(2,249+5,885)
|
28
|
|
Wake’s epochs
|
|
|
|
ACTG agree with PSG
|
3,535/(3,535+7,495)
|
32
|
|
JAWAC agree with PSG
|
7,495/(3,535+7,495)
|
68
|
The sleep parameters ([Table 4]) demonstrate the presence of a significant difference between ACTG and JAWAC estimates
of TST but not those of WASO, although when this is broken down into its two components,
LTA and WDSP, a significant difference is found with regard to the former but not
to the latter.
DISCUSSION
In the current study, we sought to assess the performance of the JAWAC, a new HST
device based on MM analysis, to identify wake and sleep state and provide estimates
of sleep parameters.
We chose to include patients who were referred to the sleep laboratory for a PSG,
regardless of the nature of the suspected sleep disorder, so as to minimize the impact
of a given sleep disorder on the performance of the device[6].
We also compared the results of JAWAC with those of both the reference standard (PSG)
and the non-reference standard (ACTG). This triangular comparison allowed a more accurate
assessment of the differences between JAWAC and ACTG.
Our results showed that JAWAC, as compared to ACTG, classified sleep and wake states
with greater specificity, while the overall accuracy and sensitivity of the two devices
were comparable. Furthermore, the use of PSG as the determining factor in disagreements
between the two HST devices showed a superiority of JAWAC in correctly identifying
the wake epochs. The fact that wake occurs more frequently in patients with sleep
breathing disorders as compared to normal subjects, regardless of its distribution
within the TIB, may explain the greater impact of the degree of specificity of such
surrogate devices, as we have defined above, on the quality of the sleep analysis
in those patients.
In the choice of parameters, we used TST as a specific measure of the duration of
sleep. We also made a distinction between different situations of wakefulness during
TIB, by designating SOL and LTA as two periods of quiet wake, distinct from WDSP,
which is a measure of the time awake during the sleep period. WASO was calculated
by adding WDSP to LTA. The sleep efficiency, which is the ratio of TST to TIB, was
reported in [table 1] but not included in the statistical analysis due to the fact that the denominator
was common for the three devices.
Set against PSG as reference, JAWAC proved efficient in distinguishing sleep from
quiet wakefulness. This was illustrated by the fact that the differences in TST, SOL,
and LTA values were not statistically significant. However, WDSP and subsequently
WASO were slightly underestimated.
In contrast, the dissimilarities between ACTG estimates and PSG measurements of all
the above sleep parameters were statistically significant; TST was overestimated whilst
SOL, LTA, WDSP, and WASO were underestimated. These results of the ACTG performance
are broadly in agreement with earlier reports, which show that actigraphies, regardless
of the manufacturer and software, tend to rate quiet wakefulness as sleep, and hence
systematically overestimate TST and underestimate SOL and WASO[5].
The results presented in this study indicated that, besides its ability to reliably
estimate TST, JAWAC was able to overcome the important problem of the recognition
of the state of quiet wakefulness.
The better performance of JAWAC in this regard is probably related to the distinct
behavior of mandibular muscles during sleep. The position of the mandible is the result
of a balance of forces between the jaw-closing and the jaw-opening muscles. The onset
of sleep has different effects on the basal activity of those muscles, where masseter
and medial pterygoid show a significant decrease in their tonic activities whereas
genioglossus and geniohyoid muscles maintain a greater phasic activity[19]. The resultant imbalance induces thus an opening movement of the jaw that is recorded
both in healthy adults and patients with obstructive sleep apneas syndrome[20],[21].
The automatic analysis algorithm used in JAWAC recognizes the onset of sleep from
a decrease in the amplitude of the jaw movements associated with a slight mouth opening
lasting for at least 2 minutes, whereas the onset of wakefulness is recognized from
a sharp increase in the amplitude of movements[10]. The ability of JAWAC to efficiently detect these specific mandibular movements
probably explains its success in identifying the state of quiet wakefulness.
This study has however some limitations, which ought to be taken into account in future
research. Just as it has been shown in the literature that actigraphies’ ability to
measure sleep may be affected to different extent within different sleep disorders.
Further studies should be carried out to evaluate the performance of JAWAC in specific
patient groups, such as in those suffering from insomnia and sleep breathing disorders.
Furthermore, in the choice of sleep parameters it seems appropriate to take into account
sleep efficiency, as this parameter is widely used in validation studies.
CONCLUSION
Our study demonstrates the ability of JAWAC to correctly identify sleep/wake epochs
and thus give an accurate estimation of various sleep parameters. This feature combined
with its capacity to record sleep respiratory events, validated in previous studies,
makes it a device unique in its ability to calculate AHI reliably using a single sensor,
a valuable asset in the science of HSTs that seeks to couple simplicity with reliability.
