Sleep monitoring is an
important issue and has drawn considerable attention in medicine and healthcare.
Traditional approaches often require subjects to stay overnight at clinics,
there has been a need for a low-cost system suitable for long-term sleep
monitoring system. Nevertheless, current monitoring methods suffer from several
drawbacks, such as obtrusiveness, lack of privacy and high-cost.
To overcome the
obtrusiveness, lack of privacy and high-cost , a non-contact and cost-effective
sleep monitoring ,is proposed for continuous recognition of the sleep status,
including on bed movement, bed exit, breathing section and heart rate
monitoring section. The emergence of
internet-of-things technology has provided a promising opportunity to build a
reliable sleep quality monitoring system by leveraging the rapid improvement of
sensors. These experimental results
indicate that it is an effective and promising solution for cost-effective
sleep monitoring system.
of sleep has a great impact on human health. There is a growing recognition of
the adverse effects from poor sleep quality and sleep disorders. Patients with
sleep disorders are prone to suffer from chronic diseases such as obesity,
diabetes, and hypertension. Sleep monitoring systems are
important to recognize sleeping disorders as early as possible for diagnosis
and prompt treatment of disease. They can provide healthcare providers with
quantitative data about irregularity in sleeping periods and durations.
¢ They can also provide detailed
sleeping profiles that depict periods of restlessness and interruptions such as
bed exits and entries due to visiting the bathroom. This information helps find
trends that correlate to certain diseases. Moreover, it enables monitoring
effectiveness of treatments to sleep-related diseases. Many studies are focused
on finding correlations between body positions during sleep to various
breathing problems (e.g., sleep apnea). So, if a sleep monitoring system can
provide fine grained information about body positions during sleep, it would
help such studies.
date, there are several methods to perform sleep monitoring such as
polysomnography (PSG), ballistocardiogram, photoplethysmography and actigraphy.
The PSG is still the primary and the most objective sleep assessment method in
clinical use, such as insomnia diagnosis. The PSG can provide fine-grained
information for sleep monitoring, thus offering more accurate sleep assessment
results. Another common alternative sleeping estimation method is actigraphy,
including an accelerometer and a memory storage chip, which can provide
information on movements during sleep. Several commercial off-the-shelf (COTS)
actigraphy based products, such as Sleep Tracker, Fit bit, and Sleep Cycle, are
publicly available. The competitive advantage of this method is that it is
convenient to deploy and inexpensive.
¢ The generated high-resolution pressure maps
can be further utilized for sleep monitoring. Nevertheless, current monitoring
methods suffer from several drawbacks, such as obtrusiveness, lack of privacy
and high-cost. These limitations prevent people from using current sleep
monitoring systems on a daily basis.
1. Automated Recognition of
Obstructive Sleep Apnea syndrome using Support Vector machine Classifier
Haitham M, Al-Angari
and Alan V.Sahakian presented the paper for the recognition of obstructive
sleep apnea syndrome using Support Vector Machine. Obstructive Sleep Apnea is a
common disorder that causes various physiological signals like heart beat
variability, oxygen saturation and respiration effort signals. The study
extracts the signals from 50 OSA patients for minute and subject
classification. Support Vector Machine was used with linear and second-order
polynomial kernels. For Subject classification , the polynomial kernel had a
clear improvement in oxygen saturation accuracy. The respiratory phase and
magnitude feature shows high sensitive in apnea minute classification compared
to other features. These features can be incorporated for portable OSA
monitoring using available respiratory and oxygen saturation devices.
2. IoT-based Wireless Polysomnography
Intelligent System for Sleep Monitoring
Lin, Mukesh Prasad, Chia-Hsin Chung , Deepak Puthal presented a paper for
wireless polysmnography intelligent system for sleep monitoring using IoT .
Polysomnography is the standard for the diagnosis of sleep apnea. An unfamiliar
environment and restricted mobility when patients tested with PSG may disturb
their sleep. This paper presents a wireless polysomnography which utilizes the
battery-powered, miniature, multipurpose recorder. The PSG signals that are
recorded helps in identifying people sleeping stages and diagnose the Sleep
Apnea. This system can facilitate the long term tracing and research of
personal sleep monitoring at home and highly portable.
3. Noncontact Vision-Based Cardiopulmonary
monitoring in different sleeping positions
H Li, Azadeh Yadollahi, Babak Taati presented a paper for the monitoring
different sleeping positions by means of noncontact Vision based
cardiopulmonary monitoring. Untreated sleeping disorders are linked to serious
medical issues including cardio vascular disease and diabetes. Current sleep
monitoring are time consuming and expensive. The system diagnose the sleep
disorder by monitoring cardiopulmonary signals during sleep. To monitor the cardiopulmonary
rates, distinctive points are automatically detected and tracked in infrared
image sequences. The optimal rates are determined using periodic measures based
on the spectral analysis.
4. Automatic Sleep monitoring using
Nakamura, Valentin Goverdovsky, Mary J. Morrell and Danilo P.Mandic presented a
paper for monitoring the sleep using Ear EEG. The system presents a long term ,
unobtrusive and affordable wearble ear
in sensor for recording the electroencephalogram. It makes an analysis scenario
for the classification of ear EEG hypnogram labels from ear EEG recordings and
the prediction of the scalp EarEEG hypnogram labels. Ear EEG carries sufficient
amount of information to faithfully represent human sleep patterns. Thus it
provides a wearable light weight device for continuous sleep monitoring.
5. Percutaneous Biphasic Electrical
Stimulation for treatment of Obstructive Sleep Apnea syndrome
Hu, Xiaomei Xu, Yongsheng Gong , Xiaofang Fan presented a paper for the
treating the Obstructive Sleep Apnea Syndrome by means of Percutaneous Biphasic
Stimulation. When the sleep apnea was detected , the genioglossus was
stimulated with the percutaneous biphasic electrical pulses that were automatically regulated by a
microcontroller to achieve the optimal effect. With the percutaneous biphasic
electrical stimulation of the genioglossus, the OSAS patients showed apnea time
decreased, RDI decreased and SaO2 increased. The stimulation of genioglossus
with percutaneous biphasic electrical current pulseis an effective method for
Obstructive Sleep Apnea as a Cause
of Systemic Hypertension
Dina Brooks, Richard L. Horner, Louise F. Kozar,
Caroline L. Render-Teixeira, and Eliot A. Phillipson presented a paper on
Obstructive sleep apnea cause of systemic hypertension. Several epidemiological
studies have identified obstructive sleep apnea (OSA) as a risk factor for
systemic hyper tension,but a direct etiologic link between the two disorders
has not been established definitively. Furthermore, the specific physiological
mechanisms underlying the association between OSA and systemic hypertension
have not been identified. The purpose of this study was to systematically
examine the effects of OSA on daytime and nighttime blood pressure (BP).
Dense Pressure Sensitive Bed sheet Design for Unobtrusive Sleep Posture
J Liu, Wenyao Xu, Ming-Chun Huang , Nabil Alshurafa presented a paper for the
dense pressure sensitive bed sheet for sleep posture. Sleep plays a pivotal role in the quality of life, and sleep posture is
related to many medical conditions such as sleep apnea. In this paper, we
design a dense pressure sensitive bedsheet for sleep posture monitoring. In
contrast to existing technique, our bedsheet system offers a completely unobtrusive
method using comfortable textile sensors. Based on high-resolution pressure
distributions from the bedsheet,we develop a novel framework for pressure image
analysis to monitor sleep postures, including a set of geometrical features for
sleep posture characterization and three sparse classifiers for posture
8. Unconstrained video monitoring of
breathing behavior and application to diagnosis of sleep apnea
Wang, Andrew Hunter, Neil Gravill, and Simon Matusiewicz presented a paper for
a new real-time automated infrared video monitoring technique for the detection
of breathing anomalies, and its application in the diagnosis of obstructive
sleep apnea. This technique include a novel motion model to detect subtle ,cyclical
breathing signals from video ,a new 3-D unsupervised self-adaptive breathing template
to learn individual’s normal breathing patterns .It avoids imposing positional
constraints on the patient, allowing patients to sleep on their back or side,
with or without facing the camera, fully or partially occluded by the bed
clothes. This paper shows that the technique achieves high accuracy in
recognizing apnea episodes and body movements and is robust to various
occlusion levels, body poses, body movement
and breathing behavior.
9. Sleep and wake classification with
actigraphy and respiratory effort using dynamic warping
Long, Member, Pedro Fonseca, J´erˆome Foussier, Reinder Haakma, and Ronald M.
Aarts proposed a paper for the use of dynamic warping (DW) methods for
improving automatic sleep and wake classi?cation using actigraphy and
respiratory effort. .D Wisan algorithm used to compare the respiratory effort
between sleep and wake states. Since the comparison occurs throughout the
entire-night recording of a subject, it may reduce the effects of within- and
between-subject variations of the respiratory effort. The results are
comparable with those obtained using an actigraphy- and cardio respiratory-based
feature set but have the important advantage that they do not require an ECG
signal to be recorded.
10. Comparison of center estimation
algorithms for heart and respiration monitoring with microwave doppler radar
Zakrzewski, HarriRaittinen, and Jukka Vanhala proposed a paper for Micro wave
Doppler radar which known for unobtrusive heart and respiration measurement. Radar
monitoring enables non-contact measurement, through clothing, of heart. This
paper shows the Levenberg-Marquardt (LM)
center estimation algorithm outperforms the state-of-the-art center estimation
algorithm precision-wise and is computationally less complex. In addition, the computational
complexity of the LM method stays almost constant as the size of the data set increases,
where as it increases exponentially. It results that the LM method is validated
both with simulations and with real data.
11. A sleep monitoring system based on
audio, video and depth information for detecting sleep events
Chao-ling Chen, Kuan-Wen Chen, Yi-Ping
Hung presented a paper to develop a
non-invasive sleep monitoring system to distinguish sleep disturbances based on
multiple sensors. A device with an infrared depth sensor, a RGB camera, and a
four-microphone array is used to detect three types of events: motion events,
lighting events, and sound events. The events are classified by an epoch
approach algorithm and provide a graphical sleep diagram. Experimental results
in sleep condition show the efficiency and reliability of our system, and it is
convenient and cost effective to be used in home context.
A Non-invasive Wearable Neck-cuff
System for Real-time Sleep Monitoring.
MahsanRefouei, Mike Sinclair, Ray Bittner presented
a paper on wearable neck-cuff system for real time sleep monitoring. Sleep is
an important part of our lives which affects many life factors such as memory,
learning, metabolism and the immune system. Researches have found that due to
lack of sleep leads to several diseases such as Chronic Obstructive Pulmonary
disease, Chronic Heart Failure, Alzheimer’s disease, etc. In order to overcome
these factors a non-invasive, wearable neck-cuff system is specially designed
and this system is capable of real-time monitoring and visualization of physiological
signals. These signals are generated from various sensors housed in a soft
neck-worn collar and sent via Bluetooth to a cell phone which stores the data.
This data is processed and reported to the user or uploaded to the cloud and/or
to a local PC.
Sleep Monitoring Through a Textile
Devot, Anna M. Bianchi, ElkeNaujokat, Martin O.Mendez, AndreasBrauers, and Sergio Cerutti presented a
paper on Sleep monitoring through a Textile recording system. The main idea of
this project is that we present a home
device for the continuous monitoring of sleep and investigate its reliability
regarding sleep evaluation. The signal used for sleep evaluation is the HRV
derived from the ECG recorded by means of a sheet and a pillow. Patients in a
sleep lab and healthy subjects at home were monitored during sleep with the
textile system, while also standard ECG and respiration were recorded. After
frequency analysis, the spectral parameters used for sleep staging was derived
at the same time from standard and textile ECG signals were compared. The
trends along the night are very similar.
Role of Actigraphy in the Evaluation of Sleep Disorders.
Peter J. Hauri, Daniel F. Kripke and PeretzLavie presented a paper on the role
of actigraphy in the evaluation of sleep disorders. The growing use of
activity-based monitoring (actigraphy) in sleep medicine and sleep research has
enriched and challenged traditional sleep-monitoring techniques. This review
summarizes the empirical data on the validity of actigraphy in assessing
sleep-wake patterns and assessing clinical and control groups ranging in age
from in fancy to elderly. An overview of sleep-related actigraphic studies is
also included. Actigraphy provides useful measures of sleep-wake schedule and
sleep quality. The data also suggest that actigraphy, despite its limitations,
may be a useful, cost-effective method for assessing specific sleep disorders,
such as insomnia and schedule disorders, and for monitoring their treatment
process. Methodological issues such as the proper use of actigraphy and
possibleartifacts have not been systematically addressed in clinical research
Unobtrusive Sleep Quality Monitoring using Smartphones.
GuoliangXing,Gang Zhou presented a paper on Sleep quality monitoring using
smartphones.The quality of sleep is an important factor in maintaining a
healthy life style. To date, technology has not enabled personalized, in-place
sleep quality monitoring and analysis. Current sleep monitoring systems are
often difficult to use and hence limited to sleep clinics, or invasive to
users. A newer idea presents, iSleep a
practical system to monitor an individual’s sleep quality using off-the-shelf
smartphone. iSleep uses the built-in microphone of the smartphone to detect the
events that are closely related to sleep quality, including body movement,
couch and snore, and infers quantitative measures of sleep quality. iSleep
adopts a lightweight decision-tree-based algorithm to classify various events
based on carefully selected acoustic features.
A non-contact and cost-effective sleep monitoring
system, SleepSense, can discriminate various sleep status stages and extract
the breathing rate accurately. In the implementation of SleepSense, we extract
different sleep status, including breathing section, bed exit, and on-bed
movement. The breathing rate, then, is calculated using a novel breathing rate
extraction algorithm. We also demonstrate the effectiveness of SleepSense in
the short-term controlled study and the 75-minute real case study. The
SleepSense can identify the on-bed movement, bed exit, breathing section, and
extract the breathing rate with an acceptable accuracy rate and wide usability.
By deploying the proposed sleep monitoring system at home, it can help people
to assess sleep quality, even diagnose the sleep disorders at the earliest
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