White Paper Sleep .pdf

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In Sleep Medicine, laboratory-based polysomnography (PSG), a multi-parametric tool, is the gold
standard for sleep monitoring. PSG simultaneously and continuously records multiple physiological
parameters throughout the night, giving clinicians an accurate sleep evaluation and diagnosis of sleep
disorders (1).
However, due the considerable mobilization of human, material and physical resources, and the long
waiting time for a test in a hospital setting, PSG is not a cost-effective process (2-4). An alternative to
PSG is the ambulatory overnight cardiorespiratory polygraphy (4). However, like PSG, this type of
testing requires the patients to sleep with numerous cumbersome sensors, cables, and equipment. It
also requires the support of specialized personnel from the sleep laboratory for setup and manual data
analysis afterwards.
For these reasons, Hexoskin biometric garments become a very interesting alternative for accurate
sleep monitoring. Hexoskin shirts are easy to wear and comfortable (5), and patients can sleep in their
bed at home in their natural environment. After a recording, the patient (or the clinician) simply
connects the device to a computer via a USB cable and use the HxServices application to synchronize
the data with the server. The data is then processed with a validated sleep algorithm, based on
documented physiological characteristics (breathing rate, heart rate variability, and body movement,
Figure 1), to label sleep stages as reported by the litterature (6,7).

Figure 1. Physiological characteristics according to the different sleep stages.

The decision tree algorithm, validated by Pion-Massicotte et al. (8,9), automatically classify sleep in
three vigilance states: Wake, NREM sleep and REM sleep, in 20-second epochs. A post-processing
correction determines sleep onset as the start of the first four consecutive minutes in NREM stage
(preceding epochs are reclassified as Wake), whereas sleep offset is determined by the end of the last
contact@hexoskin.com 1-888-887-2044
Copyright 2017 – Carré Technologies Inc. (Hexoskin)


sleep stage, lasting at least 2 consecutive minutes. It is important to note that all NREM are grouped in
one sleep stage with this algorithm1.
From this sleep stage classification, the software extracts the sleep parameters listed in table I (please
refer to Figure 2 for graphical representation of sleep-stage sequence).
Table I. Sleep parameters obtained with the Hexoskin sleep monitoring system.

Sleep data
Total Sleep Time

Time in each stage


Sleep efficiency

Sleep percent

The time spent in any sleep phase (i.e. not awake).
SleepTotalTime = Time in non-REM + Time in REM
Time awake (wake after sleep onset): The total time in the awake vigilance state
during the sleep period, which is defined as the time between the sleep onset and
the last awakening (Figure 2).
Time in non-REM: The total time in the NREM sleep phase during the sleep
Time in REM: The total time in the REM sleep phase during the sleep period.
Graph of sleep stages in function of time (20 seconds epoch).
Proportion of time asleep divided by the time in bed (lying position detected). The
sleep efficiency is normally over 95%. A value under 85% is generally associated
as a bad night.
Sleep efficiency (%) = Total Sleep Time / Time in bed * 100
Percentage of time asleep divided by the sleep period defined as the time between
falling asleep and waking up.
Sleep percent (%) = Total Sleep Time / Sleep period * 100

Sleep latency

Time to fall asleep from the sleep activity start (lying position detected or sleep start
annotation) to the first epoch of sleep detected.

Sleep position

Number of sleep position changes detected during the night.

Time spent in each

SleepPositionP1Time: Total time where user is in the sleep position 1 (Belly)
SleepPositionP2Time: Total time where user is in the sleep position 2 (Back)
SleepPositionP3Time: Total time where user is in the sleep position 3 (Right)
SleepPositionP4Time: Total time where user is in the sleep position 4 (Left)
SleepPositionP5Time: Total time where user is in the sleep position 5 (Standing)

Sleep positions

Graph of sleep positions in function of time.


Development and Validation of an Algorithm for the Study of Sleep Using a Smart Shirt
Pion-Massicotte, J. Forum MEDTEQ, 2014.
A validation study was carried on 21 participants (healthy adults between 18-25 years old without sleep disorders) in a sleep laboratory for a
two-night protocol. The participants were set up for a PSG while simultaneously wearing the Hexoskin biometric shirt. PSG sleep data was
recorded and scored according to Rechtschaffen and Kales (1968), using 20-s epochs. In conclusion, the study validates with an epoch-byepoch comparison the Hexoskin classification algorithm against the current gold standard (PSG) for the detection of three different sleep
contact@hexoskin.com 1-888-887-2044
Copyright 2017 – Carré Technologies Inc. (Hexoskin)



* Wake after sleep onset

* *





Time in bed (min)
Time to fall asleep(min)

Sleep period (min)

Figure 2. Graphical representation of sleep-stage sequence obtained with the Hexoskin technology.

Hexoskin provides long-term ergonomic monitoring centered on the patient. There are several
applications for sleep monitoring using Hexoskin technology (Figure 3). Clinicians and researchers use
it for physiological studies over a long periods of time in a clinical setting and for research projects.
The Hexoskin platform is also used by patients for at-home monitoring, allowing a more reliable
reflection on their normal sleep patterns. Data collected with Hexoskin technology is also used to better
understand human health beyond sleep analysis, for psychology, cardiology, and respiratory diseases.

Figure 3. Potential clinical, research and patient care applications of the Hexoskin technology.

contact@hexoskin.com 1-888-887-2044
Copyright 2017 – Carré Technologies Inc. (Hexoskin)


1) R. B. Berry, “Fundamentals of sleep medicine”, Elsevier Saunders, 2012.
2) W. W. Flemons et al., “Access to Diagnosis and Treatment of Patients with Suspected Sleep
Apnea”, Am J Respir Crit Care Med, vol. 169, pp. 668–672, 2004.

3) B. W. Rotenberg, et al., “Wait times for sleep apnea care in Ontario: A multidisciplinary
assessment”, Can Respir J, vol.17(4), pp.170-174, 2010.
4) J. Corral-Penãfiel et al., “Ambulatory monitoring in the diagnosis and management of
obstructive sleep apnoea syndrome”, Eur Respir Rev, vol. 22, pp. 312–324, 2013.
5) E. M. Harrison et al., “Efficacy of a smart textile shirt: developing a sleep health screening tool
for military populations”, SLEEP, vol. 28, #0384, 2015.
6) C. D. Harris, "Neurophysiology of sleep and wakefulness," Respir Care Clin N Am, vol. 11, pp.
567-86, 2005. 

7) M. A. Carskadon et al., "Normal human sleep: an overview," Principles and practice of sleep
medicine, vol. 4, pp. 13-23, 2011. 

8) J. Pion-Massicotte, “Development and validation of an algorithm to do sleep characterization
with the Hexoskin smart shirt”, Technical Report for Carré Technologies Inc., 2016.
9) J. Pion-Massicotte, “Mise au point et validation d’un algorithme pour caractériser le sommeil à
l’aide du vêtement intelligent Hexoskin”, Master thesis of Institute of Biomedical Engineering,
Polytechnique Montréal, 2014.

contact@hexoskin.com 1-888-887-2044
Copyright 2017 – Carré Technologies Inc. (Hexoskin)


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