Author: Chih-Ming Cheng (2007-07-11); recommended: Yeh-Liang Hsu (2007-07-11).
Note: This article is Chapter 1 of
Chih-Ming Cheng’s PhD thesis “Development
of a portable system for
tele-monitoring of sleep in a home environment”.
Chapter 1. Introduction
An introduction to sleep
Sleep is a basic
physiological need of all humans and is characterized by a reduction in
voluntary body movement and decreased reaction to external stimuli. Some early
theories saw sleep as an intermediate state between awakeness and death [MacNish,
1834]. With the improvement of electrophysiological techniques, specifically
the electroencephalogram (EEG), it was determined that the brain is not
inhibited during sleep. Sleep is now seen as a unique state of consciousness. Dr.
Nathaniel Kleitman, now known as the “Father of American sleep research,” described
sleep as a periodic temporary cessation, or interruption, of the waking state,
which is the prevalent mode of existence for the healthy human adult [Kleitman,
sleep into REM (rapid eye movement) and 4 NREM (non-rapid eye movement) sleep
stages. REM sleep accounts for 20~25% of total sleep time and is associated
with dreaming. NREM sleep accounts for 75~80% of total sleep time and is
further subdivided into light sleep (Stage I and Stage II) and deep sleep
(Stage III and Stage IV). Sleep proceeds in cycles of NREM and REM phases. In
humans, the cycle of REM and NREM is approximately 90 minutes.
There are many
theories about the functions of sleep. Researches show that REM sleep and NREM
sleep play different roles. Hartmann  stated that deep sleep stage of
NREM has a physically restorative function after exercise, injury, or physical
tiredness and is related to anabolism and synthesis of macromolecules. On the
other hand, REM sleep has a restorative function with focused attention and
emotional adaptation to physical and social environment. Marks et al. 
pointed out that REM sleep is particularly important to the developing brain
and is necessary for proper central nervous system. Studies investigating the
effects of REM sleep deprivation have shown that deprivation early in life can
result in behavioral problems, permanent sleep disruption, decreased brain mass
[Mirmiran et al., 1983], and result in an abnormal amount of neuronal cell
death [Morrissey, Duntley and Anch, 2004]. An increasing number of theorists
showed a strong relationship between sleep and memory. [Siegel, 2001, Stickgold,
et al., 2001, Gais and Born, 2004]. In summary, sleep is related with
functional status, general and mental health. [Briones et al., 1996, Cauter, et
al., 1999, Ashmana, et al., 1999, Meijer, et al., 2001]
discovery of electrical brain wave activity by Hans Berger in 1929, the
electroencephalogram (EEG) has become a clinical and diagnostic tool for brain
dysfunction. By using the EEG, different stages of sleep were classified in
1937 [Loomis et al., 1937]. After discovery of the REM (rapid eye movement) sleep
periods by Aserinsky and Kleitman in 1953 and the demonstration of periodical
sleep cycles by Dement and Kleitman, polysomnography was established as the
major diagnostic tool in sleep disorders [Aserinsky and Kleitman, 1953, Dement
and Kleitman, 1957]. Sleep stage is currently evaluated based on the Rechtschaffen
and Kales (R-K) method [Rechtschaffen and Kales, 1968].
consists of a simultaneous recording of multiple physiologic parameters related
to sleep and other physiologic disorders related to sleep. A polysomnogram usually
has several channels include the following:
Electroencephalography (EEG): To
evaluate sleep stage.
Electrooculogram (EOG): To
monitor both horizontal and vertical eye movements.
Electromyography (EMG): To
detect periodic limb movements of sleep
Airflow, respiratory effort,
sound, blood oxygen saturation (SaO2): To observe sleep-related breathing
disorder, such as snoring and obstructive sleep apnea syndrome (OSAS).
Other parameters: electrocardiograph
(ECG), temperature, light and so on.
Due to the development of sensing technology, small, portable, non-conscious
and tele-monitoring devices, are new approaches for sleep monitoring.
Sleep-related breathing disorders, body movements, sleep stage and sleep
quality are main research fields of sleep. These issues will be discussed in
details in the following sections 1.2-1.5.
Snoring is a
very common problem and a possible sign of sleep-related breathing disorder.
Intermittent snoring and drops of SaO2 are characteristic features of OSAS. In
clinical practice, PSG recording is used as the standard evaluation procedure
for sleep-related breathing disorder (SRBD). Patients have to wear SaO2
saturation, nasal airflow, thoracic effort, and sound sensors to do one-night
tests in a specialized laboratory. Snoring and OSAS symptoms are identified by
off-line diagnosis software in a computer.
computer-based systems have been developed to quantitatively measure snoring
using acoustic sounds. Jane et al.  designed an automatic algorithm for
detecting acoustic snoring signals based on a neural network. The input pattern
of the neural network, which consists of 22 temporal and spectral features of
each sound segment, distinguishes between the snoring sound and other
respiratory sounds. Figure 1-1 shows a snore recording with 500,000 data
(sampling frequency 5000 Hz), 15 snorers of a normal snorer were detected. In
their validation test, more than 500 snores were randomly taken from a database
of 30 patients and analyzed. The average sensitivity of the algorithm was 82%
and the average positive prediction value was 90%. Solà-Soler et al. 
used a logistic regression model to classify normal snorers and OSAS patients
by observing their sound intensity and other spectral parameters. The model’s
parameters were adjusted to correctly classify 100% of the OSAS patients at the
expense of 57.1% of normal snorers.
Figure 1-1. Detection of snores of a normal snorer
[Jane et al., 2000]
Due to the
development of microprocessor technology, some portable systems have been
designed for monitoring snoring and OSAS. Cohen  presented algorithms
utilizing a microprocessor for the quantitative and objective analysis of
acoustical pulmonary signals, such as breathing and snoring sounds. Penzel et al.
[1990, 1991] developed a digital recording device, called MESAM IV (MAP; Martinsried, Germany) to monitor oxygen
saturation, heart rate, snoring and body position in order to screen patients
for the presence of OSAS. MESAM IV records snoring sounds by means of a
laryngeal microphone. If the proportion of sounds between 50Hz and 800Hz
exceeds 50%, it is assumed that patients are snoring. Intermittent snoring is
defined as intervals between two detected snores that last between 5 seconds
and 60 seconds. The diagnosis of OSAS was established by calculating oxygen
desaturation index, heart rate variation index and intermittent snoring index.
Following this development, a number of validation studies on MESAM IV were
presented [Stoohs and Guilleminault, 1992, Koziej, et al., 1994, Esnaola, et
al, 1996, Verse, et al, 2000, Cirignotta, et al., 2001]. In these studies, the
intermittent snoring index was found to have high sensitivity (92%-96%) but low
specificity (16%-27%). MESAM IV can record for periods of up to 18 hours, which
makes it possible to perform examinations in a patient’s home environment and
then to be diagnosed in the hospital.
Audio alarms and
stimulators are widely used to stop snoring or apnea processes during sleep.
Kermit et al. [Kermit, et al., 1995] developed a device for early online
detection of upper airway obstructions. If an obstruction is detected, an audio
alarm alerts the patient to prevent the occurrence of apnoeic events. Çavuşoğlu
et al.  also proposed a similar approach to alarm a patient when the
voltage of a nasal air flow sensor was below the threshold value. Miki et al.
 reported positive results with electrical stimulation in patients with
OSAS. In their research, when an apnea lasted more than 5 seconds, electrical
pulses of 0.5 ms (repetition rate, 50 Hz) and 15 to 40 volts were delivered
through bipolar electrodes attached to the skin. The electrical pulses stopped
immediately after breathing resumed or after 10 seconds. As a result, the
number of times per hour that oxygen saturation dropped below 85% decreased
significantly. Guilleminault et al.  also presented similar procedures
with stimulations that started within 5 seconds of abnormal breathing and
stopped with the resumption of normal breathing.
1.3 Body movements
low motor activity levels and prolonged episodes of uninterrupted immobility
are associated with increasing sleep depth. There are also motor disturbances
that are triggered by sleep such as restless legs syndrome (RLS) and periodic
limb movements during sleep (PLMS). Such symptoms disrupt sleep and cause
daytime tiredness and sleepiness.
and unrestrained sensing techniques have been developed for the monitoring of
movement during sleep. The use of load cells or force sensors is the most
common approach to detect movements on bed. Load cells represent a simple and
durable technology, which is used in several researches [Wheatley, et al.,
1980, Barbenel, et al., 1985, Choi, et al., 2004, Adami, 2005]. The use of force
sensor is also a popular technique for monitoring movements in bed. Nishida et
al.  presented the idea of a robotic bed, which is equipped with 221
pressure sensors for monitoring of respiration and body position (Figure 1-2). Van
der Loos et al.  also proposed a similar system called SleepSmart™,
composed of a mattress pad with 54 force sensitive resistors and 54 resistive
temperature devices, to estimate body center of mass and index of restlessness.
These large-size equipments are not easy to set up and can be used in specific laboratories
Figure 1.2. Robotic bed [Nishida, 1997]
solutions have been proposed. Several authors have employed the static charge
sensitive bed (SCSB) for monitoring of motor activity. The SCSB is composed of
two metal plates with a wooden plate in the middle that must be placed under a
special foam plastic mattress, which operates like a capacitor [Alihanka and
Vaahtoranta, 1979, Rauhala, et al., 1996, Kaartinen, et al., 2003]. Watanabe et
al.  designed a pneumatics-based system for sleep monitoring. A thin,
air-sealed cushion is placed under the bed mattress of the subject and the
small movements attributable to human automatic vital functions are measured as
changes in pressure using a pressure sensor (Figure 1-3).
Figure 1-3. A pneumatics-based system for sleep
monitoring [Watanabe et al., 2005]
techniques, such as optical fibers and conductive fibers are also been used for
monitoring of movement on bed. Tamura  et al, proposed a body movement monitoring
system using optical fibers. Kimura et al.  designed an unobtrusive vital
signs detection system, which uses conductive fiber sensors to detect body
position, respiration, and heart rate. These fiber sensors can be incorporated
in a conventional bed sheet for home use (Figure 1-4).
Figure 1-4. An unobtrusive vital signs detection
system, which uses conductive fiber sensors [Kimura et al., 2004]
Sleep stage and sleep quality
is considered to be the “gold standard” for assessing sleep. Different sleep
stages are evaluated by medical specialists using polygraph data such as
electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG)
based on the Rechtschaffen and Kales (R-K) method. This technique usually requires
individuals to sleep in research laboratories which are known to change
habitual sleep patterns [Reynolds, et al., 1992] and induce a “first-night”
effect [Riedel, et al., 1998].
response monitoring methods have been proposed for the evaluating of sleep/wakefulness
[Bonato, et al., 1995, Blood, et al., 1997]. In the behavioral response
monitoring paradigm, a threshold intensity visual or auditory stimulus
generated by a computer was presented about once per minute, and subjects
pressed a microswitch if the stimulus was detected.
between heart rate variability and sleep stages has also been discovered. Ichimaru
 et al. observed that the RR-interval was shorter during REM sleep than
during NREM sleep. Méndez  et al. proposed a time-varying autoregressive model
to separate the REM and NREM sleep epochs. Watanabe  et al. designed a noninvasive
and unrestrained pneumatic biomeasurement system, based on an air cushion and
pressure sensor, to evaluate heart rate. In this system, sleep depth is given
by linear functions of heart rate and the different between wake and REM sleep
stages are discriminated by body movement data.
Since the development
of the actioculographic monitor in 1979, a novel method to estimate sleep stage
through body movements have been suggested. Measurement of motility has become a
popular method in the study of human sleep. Kayed et al  used three
criteria, eye movements, body movements, and submental electromyogram, to
identify awake, REM sleep, and NREM sleep (Figure 1-5). Based on this
development, a wearable wrist actigraphy, have been developed for the identification
of awake, REM and NREM stages [Hauri and Wisbey, 1992, Emilia, et al., 1999, Ancoli-Israel,
et al., 2003]. Ajilore et al.  proposed a home-based sleep monitoring
system, called Nightcap, which uses eyelid and body movement sensors to
discriminate wake, NREM, and REM sleep automatically. Literatures show that
actigraphy is a valuable device to detect sleep-wake period between actigraphy
and PSG ranging from 80% to 90% [Hauri and Wisbey, 1992; Sadeh, et al., 1995].
Although estimating sleep stages through body movement is not as accurate as
PSG, studies are in general agreement that measures with body movement were
fairly sensitive in detecting sleep.
Figure 1-5. Different body activity patterns of
awake, N-REM and REM stages [Kayed et al., 1979]
Similar to wrist
actigraphy, Choi et al.  introduces a bed actigraphy for user-friendly
sleep-wake monitoring (Figure 1-6). An automatic scoring algorithm scored each
epoch of the recordings for either ‘wake’ or ‘sleep’ by the signals of 4 load
cells, which are installed at each corner of the bed. Bed actigraphy has some
advantages over ordinary actigraphic devices. First, the bed actigraphy does
not constrain the subjects during recording. The subject can even be unaware of
the recording process. Secondly, bed actigraphy can detect the overall
movements of the subject by measuring the body activity at four points, while
an actigraph detects only the movements of the body part on which it is
mounted. The bed actigraphy shows good agreement (95.2%) but medium sensitivity and predictive value (64.4%, 66.8%).
Figure 1-6. The bed actigraphy system [Choi et
refers both the subjective assessment given by the subject of how restorative
and undisturbed his/her sleep has been (via a standardized questionnaire) or to
a series of objective measures (from self-report, wrist actigraphy monitoring, “Nightcap”
monitoring, polysomnogram, etc.). The most commonly used measure of sleep
quality is the Pittsburgh
sleep quality index (PSQI), proposed by Buysse et al. in 1989. Nineteen
individual items generate seven "component" scores: subjective sleep
quality, sleep latency, sleep duration, habitual sleep efficiency, sleep
disturbances, use of sleeping medication, and daytime dysfunction. The sum of
scores for these seven components yields one global score of 1-21.
Peng et al.
 proposed a system which consists of heart-rate, video, and audio
sensors, and apply machine learning methods to infer the sleep-awake condition
during the time a user spends on the bed (Figure 1-7). The PSQI Scores of three
objective components (sleep latency, sleep duration, and habitual sleep
efficiency) can be estimated through the asleep/awake detection. Similar
procedures, which estimate sleep quality with the PSQI via sleep-awake stages,
are suggested by several authors [Aritomo, et al., 1999, Wakoda, et al., 2005].
Figure 1-7. System for asleep-awake detection [Peng
et al., 2006]
Tele-monitoring and the PTMS structure
For the needs of
remote monitoring of sleep, several research activities are underway. Kristo et
al.  presented a telemedicine protocol for the online transfer of PSGs
from a remote site to a centralized sleep laboratory, which provided a
cost-saving approach for the diagnosis of OSAS. Seo et al.  developed a non-intrusive
health-monitoring house system to monitor patients’ electrocardiogram results,
weight, movement pattern and snoring, in order to get the information of
patients’ health status and sleep problems. Choi et al.  presented a ubiquitous
health monitoring system in a bedroom, which monitors ECG, body movements and
snoring with non-conscious sensors.
A centralized framework
is used in most home tele-health systems, in which a centralized database is
used for data storage and analysis. Figure 1-8 shows the structure of the “Portable
Telehomecare Monitoring System (PTMS)” developed by the authors [Hsu, et al.,
2007]. The PTMS is a decentralized home tele-health system. Instead of using a
centralized database that gathers data from many households, a single household
is the fundamental unit for sensing, data transmission, storage and analysis in
the PTMS. The monitoring data is stored in the “Distributed Data Server (DDS)”
inside a household.
As shown in
Figure 1-8, sensing data from sensors embedded in the home environment are
transmitted to the DDS. Sensing signals are then processed and stored in the
DDS. Authorized remote users can request data from the DDS using an Internet
web browser (through an application server) or a Visual Basic (VB) program
(direct access to the DDS). Event-driven messages (mobile phone text messages
or emails) can be sent to specified caregivers when an urgent situation is
Figure 1-8. The structure of the PTMS
several advantages of the PTMS structure over the traditional centralized
The scale of the PTMS is much
smaller, which makes it economically viable and acceptable to the end-users. A
single household can be a running unit of the PTMS. This distributed structure
can be adapted if a centralized database is needed.
Instead of sending the health
monitoring data to a centralized database in a home health care provider,
health monitoring data are stored within the household. Only authorized
caregivers can access the data. Privacy is better protected.
The route from the sensor to
server is much shorter. Data transmission is easier and more reliable. When the
Internet communication fails, the local system can still function normally and
keep collecting data. Thus data integrity is better preserved.
Purpose of this research
research activities are underway in the development of sleep monitoring
systems. However, a basic problem is that, it requires specialists with a high
degree of technical expertise and the use of an expensive polygraph. Nowadays, small,
portable, non- non-invasive, non-constrictive, non-conscious and remote
monitoring techniques are new approaches for sleep monitoring at home.
describes the development of a tele-monitoring system for sleep (called the
Sleep Guardian system) based on the PTMS structure. This system embraces a
snoring and OSAS symptoms detector (SOD), a physical activity detecting system
(PAD-Mat) and a sleep diagnosis interface. The Sleep Guardian system on-line
monitors the symptoms of sleep-related disorders, such as snoring, OSAS, PLMS,
and evaluates sleep stages and sleep quality via physical activity data. This
portable monitoring system is to be used as a home appliance as a precautionary
measure. Patients who are classified to have sleep disorders by the Sleep
Guardian system should consult doctors for further diagnosis.
Figure 1-9 shows
the structure of the Sleep Guardian system developed in this research. Similar
to the PTMS structure in Figure 1-8, the core components of the SOD and PAD-Mat
are DDSs which analyze bio-signals captured by sensors and classify symptoms by
detecting algorithms. The DDS consists of a PIC server mounted on a peripheral
application board. The PIC server integrates a PIC microcontroller (PIC18F6722, Microchip), EEPROM (24LC1025,
Microchip) and a networking IC (RTL8019AS, Realtek). It provides networking
capability and can be used as a web server. Users and caregivers can access the
sleep guardian system for historical monitoring data via the Internet for
further analysis. This tele-monitoring system provides a convenient approach to
better understand and recognize sleep-related problems.
Figure 1-9. Structure of the Sleep Guardian system
The rest of this
dissertation is organized as follows. Chapter 2 and Chapter 3 present the
development of the PAD-Mat and the SOD. Chapter 4 proposes a network framework and
a sleep diagnosis interface of the Sleep Guardian system. Chapter 5 describes
the applications and benefit of the Sleep Guardian system.
Hayes, T.L., Pavel, M., Singer, C.M., “Detection and classification of movements
in bed using load cells”, IEEE 27th
Annual International Conference of the Engineering in Medicine and Biology
Society, 2005, pp.589-592（論文出處的期刊要改成斜體字）
Stickgold, R., Rittenhouse, C.D., Hobson, J.A., “Nightcap: laboratory and
home-based evaluation of a portable sleep monitor”, Psychophysiology, 1995, v. 32,
n. 1, pp.92-98.
and Vaahtoranta, K., “A Static Charge Sensitive Bed - A New Method for
Recording Body Movements during Sleep”, Electroencephalography and Clinical
Neurophysiology, 1979, v. 46, pp. 731-734.
Cole, R., Alessi, C., Chambers, M., Moorcroft, W., Pollak, C.P., ”The role of
actigraphy in the study of sleep and circadian rhythms”, Sleep, 2003, v. 26, n.
3, pp. 342-92.
Yonezawa, Y., and Caldwell,
W.M., “A wrist-mounted activity and pulse recording system”, Proceedings of the
First Joint Engineering in Medicine and Biology, 1999, v. 2, pp. 693.
Aserinsky, E., and Kleitman, N.,
“Regularly occurring periods of eye motility, and concomitant phenomena, during
sleep”, Science, 1953, v. 118, n. 3062, pp. 273-274.
Monkb, T.H., Kupferb, D.J., Clarka, C.H., Myersa, F.S., Frankb, E., and
Leibenluftc, E., “Relationship between sleep and mood in patients with
rapid-cycling bipolar disorder”, Psychiatry Research, 1999, v. 86, pp. 1-8.
Barbenel, J.C., Ferguson-Pell,
M.W., and Beale, A.Q., “Monitoring the mobility of patients in bed”, Medical
and Biological Engineering and Computing, 1985, v. 23, n. 5, pp. 466-468.
Bonato, R.A. and
Ogilvie, R.D., “A home evaluation of a behavioral response measure of
sleep/wakefulness”, Percept Mot Skills, 1989, v. 68, n. 1, pp. 87-96.
Sack, R.L., Percy, D.C. and Pen, J.C., “A comparison of sleep
detection by wrist actigraphy, behavioral response, and polysomnography”, Sleep,
1997, v. 20, n. 6, pp. 388-395.
Adams, N., Strauss, M., Rosenberg,
C., Whalen, C., Carskadon, M., Roebuck, T., Winters, M., and Redline, S., “Relationship
between sleepiness and general health status”, Sleep, 1996, v. 19, n. 7, pp.
Reynolds, C.F., 3rd, Monk, T.H., Berman, S.R., and Kupfer, D.J., “The
Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and
research”, Psychiatry Research, 1989, v. 28, n. 2, pp. 193-213.
Cauter, E.V. and
Spiegel, K., “Sleep as a mediator of the relationship between socioeconomic
status and health: A hypothesis”, Annals of the New York Academy of Sciences,
1999, v. 896, pp. 254-261.
Çavuşoğlu, M., Eroğul,
O., Telatar, Z., “Design And Implemantation of A Programmable Apnea Monitoring
System”, European Signal Processing Conference, Sep 2005.
Choi, B H., Seo,
J. W., Choi, J. M., Shin, H. B., Lee, J. Y., Jeong, D. U. and Park, K. S., “Non-constraining
sleep/wake monitoring system using bed actigraphy”, Medical and biological
engineering and computing, 2007, v. 45, n. 1, pp. 107-14.
J.M.; Choi, B.H.; Seo, J.W.; Sohn, R.H.; Ryu, M.S.; Yi, W.; Park, K.S., “A
System for Ubiquitous Health Monitoring in the Bedroom via a Bluetooth Network
and Wireless LAN”, Engineering in Medicine and Biology Society, 2004. EMBC
2004. Conference Proceedings. 26th Annual International Conference of the IEEE,
v. 2, 2004, p. 3362-3365.
Mondini, S., Gerardi, R., Mostacci, B., Sancisi, E., “Unreliability of
automatic scoring of MESAM 4 in
assessing patients with complicated obstructive sleep apnea syndrome,” Chest, v.199,
n.5, 2001, p.1387-1392.
Cohen, A., “Signal
processing methods for upper airway and pulmonary dysfunction diagnosis,” IEEE
Engineering in Medicine and Biology Magazine, March 1990, p.72-75.
Dement, W. and
Kleitman, N., “Cyclic variations of EEG during sleep and their relation to eye
movements, body motility and dreaming”, Electroencephalography and Clinical
Neurophysiology, 1957, v. 3, pp.673-690.
Monica, Z., Christophe, P., and Jean, K., “Actigraphy and Leg Movements During
Sleep: A Validation Study”, Journal of Clinical Neurophysiology., 1999, v. 16,
n. 2, pp.154-160.
Duran, J., InfanteRivard, C., Rubio, R., Fernandez, A., “Diagnostic accuracy of
a portable recording device (MESAM IV) in suspected obstructive sleep apnoea,”
European Respiratory Journal, v.9, n.12, 1996, p.2597-2605.
Gais, S., and
Born, J., “Declarative memory consolidation: Mechanisms acting during human
sleep”, 2004, Learning and Memory, v. 11, pp. 679-685.
Powell, N., Bowman, B., Stoohs, R., “The effect of electrical stimulation on
obstructive sleep apnea syndrome”, Journal of Medical Engineering &
Technology, v.26, n. 6, Nov 2002, p, 259-264.
Hartmann, E. L.,
The functions of sleep, 1973, Yale University Press.
Wisbey. J., “Wrist actigraphy in insomnia”, Sleep, 1992, v. 15, n. 4, pp. 293-301.
Hsu, Y. L.,
Yang, C. C., Tsai, T. C., Cheng, C. M., Wu, C. H., “Development of a
Decentralized Home Telehealth Monitoring System”, Telemedicine and e-Health, v.
13, n.1, 2007, p. 69-78.
Ichimaru, Y., Clark, K.P., Ringler, J., and Weiss, W.J., “Effect of
sleep stage on the relationship between respiration andheart rate variability”,
Proceeding of the Computers in Cardiology, 1990, pp. 657-660.
Sola-Soler, J., Fiz, J.A., Morera, J., “Automatic detection of snoring signals:
validation with normal snorers and OSAS patients,” Annual International
Conference of the IEEE Engineering in Medicine and Biology - Proceedings, v 4, 2000,
Kaartinen, J., Kuhlman,
I., and Peura, P., “Long-term Monitoring of
Movements in Bed and Their Relation to Subjective Sleep Quality”, Sleep and
Hypnosis, 2003, v. 5, n. 3, pp. 145-153.
Kayed, K., Hesla,
P.E., Rosjo, O., “The actioculographic monitor of sleep”, Sleep, 1979, v. 2, n.
Kermit, M., Eide,
A. J., Lindblad, T., “Early online detection of upper airway obstructions in
obstructive sleep apnoea syndrome (OSAS) patients”, Chest, v. 107, 1995, n.1, p.67-73.
Kobayashi, H., Kawabata, K., and Van der Loos, H.F., “Development of an
unobtrusive vital signs detection system using conductive fiber sensors”, Proceedings.
2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004,
v. 1, pp. 307- 312.
Kleitman, N., Sleep
and wakefulness, 1939, The University of Chicago Press, Chicago.
Cieslicki, J. K., Gorzelak, K., Sliwinski, P., Zielinski, J., “Hand-scoring of
MESAM-4 recordings is more accurate than automatic-analysis in screening for
obstructive sleep-apnea,” European Respiratory Journal, v.7, n.10, 1994, p.1771-1775.
Kristo, D. A.,
Eliasson, A. H., Poropatich, R. K., Netzer, C. M., Bradley, J. P., Loube, D. I.
and Netzer, N. C., “Telemedicine in the Sleep Laboratory: Feasibility and
Economic Advantages of Polysomnograms Transferred Online”, Telemedicine Journal
and e-Health, v. 7, n. 3, Sep 2001, p. 219-224.
Loomis, A., Harvey,
E., and Hobart, G., “Cerebral states during sleep as studied by human brain
potentials”, Journal of experimental psychology, 1937, v. 21, pp.127-144.
MacNish, R., The
philosophy of sleep, 1834.
Shaffery, J.P., Oksenberg, A., Speciale, S.G., and Roffwarg, H.P., "A
functional role for REM sleep in brain maturation", Behavioural Brain Research,
1995, v. 69, pp. 1-11.
Meijer, A.M., Habekothé,
R.T., and Wittenboer, G.L.H.V.D., “Mental health, parental rules and sleep in
pre-adolescents”, Journal of Sleep Research, 2001, v. 10, pp. 297–302.
Méndez, M., Bianchi,
A.M., Villantieri, O., and Cerutti, S., “Time-varying Analysis of the Heart
Rate Variability during REM and Non REM Sleep Stages”, Proceedings of the 28th
IEEE EMBS Annual International Conference, 2006, pp. 3576-3579.
Miki, H., Hida,
W., Chonan, T., Kikuchi, Y., Takishima, T., “Effects of submental electrical
stimulation during sleep on upper airway patency in patients with obstructive
sleep apnea”, Am Rev Respir Dis., v. 140, n. 5, Nov 1989, p.285-9.
Scholtens, J., van de Poll, N., Uylings, H., van der Gugten, J., and Boer, G.,
"Effects of experimental suppression of active (REM) sleep during early
development upon adult brain and behavior in the rat", 1983, Brain Research,
v. 283, pp. 277-286.
Duntley, S.P., Anch, A.M., and Nonneman, R., “Active sleep and its role in the
prevention of apoptosis in the developing brain”, Medical Hypotheses, 2004, v. 62,
n. 6, pp.876-879.
Takeda, M., Mori, T., Mizoguchi, H. and Sato, T., 1997, “Monitoring patient
respiration and posture using human symbiosis system,” Proceedings of the 1997
IEEE International Conference on Intelligent Robots and Systems, 1997, v. 2,
Peng, Y.T., Lin,
C.Y., and Sun, M.T., “Sleep condition inferencing using simple multimodality sensors”,
Proceedings. 2006 IEEE International Symposium on Circuits and Systems, 2006, pp.
Amend, G., Meinzer, K., Peter, J. H., von Wichert, P., “MESAM – A heart-rate
and snoring recorder for detection of obstructive sleep-apnea,” Sleep, v.13,
n.2, 1990, p.175-182.
Althaus, W., Meinzer, K., Peter, J. H., von Wichert, P., “A device for
ambulatory heart rate, oxygen saturation and snoring recording,” Annual
International Conference of the IEEE Engineering in Medicine p.1616-1617.
Rauhala, E., Erkinjuntti,
M., and Polo, O., “Detection of Periodic Leg Movements with a
Static-Charge-Sensitive Bed”, Journal of Sleep Research, 1996, v. 5, pp.
246-250. and Biology Society, v.13, n.4, 1991,
A. and Kales, A., “A Manual of Standardized Terminology, Techniques and Scoring
System for Sleep Stages of Human Subjects”, 1968, US. Dept. of Health Education
and Welfare, Bethesda, MD.
Reynolds, C. F.,
III Grochocinski, V. J., Monk, T. H., Buysse, D. J., Giles, D. E., Coble, P. A.
et al. Concordance between habitual sleep times and laboratory recording
schedules. Sleep, 1992, v. 15, pp. 571–575.
Riedel, B. W.,
Winfield, C. and Lichstein, K. L., “Anxiety and first night effect in older
insomniacs”, Sleep, 1998, v.21, pp. 131.
Hauri, P.J., Kripke, D.F. and Lavie, P., “The role of actigraphy in the
evaluation of sleep disorders”, Sleep, 1995, v. 18, pp. 288-302.
Choi, J., Choi, B., Jeong, D.U. and Park, K., “The development of a
nonintrusive home-based physiologic signal measurement system”, Telemedicine
Journal and e-Health, v. 11, n. 4, Aug 2005, p.487-95.
Siegel, J.M., “The
REM sleep-memory consolidation hypothesis”, Science, 2001, v. 294. n. 5544, pp.
Jane, R.; Fiz, J.A.; Morera, J., “Variability of snore parameters in time and
frequency domains in snoring subjects with and without Obstructive Sleep Apnea”,
Annual International Conference of the Engineering in Medicine and Biology
Society, Sept. 2005, p. 2583 – 2586.
Hobson, J.A., Fosse, R., and Fosse, M., “Sleep, learning, and dreams: Off-line memory
reprocessing”, Science, 2001, v. 294, pp.1052-1057.
Stoohs and R.,
Guilleminault, C., “MESAM 4: an ambulatory device for the detection of patients
at risk for obstructive sleep apnea syndrome (OSAS)”, Chest, v.101, n.5, 1992, p.1221-1227.
Tamura, T., Nishigaichi, A., and Nomura,
T., “Monitoring of body movement during sleep in bed,” Proceedings of the
Annual International Conference of the IEEE Engineering in Medicine and Biology
Society, 1992, v.14, pp. 1483-1484.
Van der Loos,
H.F., Ullrich, N., and Kobayashi, H., “Development of Sensate and Robotic Bed
Technologies for Vital Signs Monitoring and Sleep Quality Improvement”, Autonomous
Robots, 2004, v. 15, n. 1, pp.67-79.
Pirsig, W., Junge-Hulsing, B., Kroker, B., Chest, v.117, n.6, 2000, p.1613-1618.
Fukuda, T.; Arai, F.; Hasegawa, Y.; Noda, A.; and Waguchi, M., “Adaptive human
interface for refreshing sleep based on biological rhythm”, 2005, pp. 3052-3056.
Watanabe, T., Watanabe, H., Ando, H., Ishikawa, T., and Kobayashi, K., “Noninvasive
measurement of heartbeat, respiration, snoring and body movements of a subject
in bed via a pneumatic method”, IEEE Transactions on Biomedical Engineering,
2005, v. 52, pp. 2100-2107.
and Watanabe, K., “Noncontact method for sleep stage estimation”, IEEE Transactions
on biomedical engineering, 2004, v. 51, n. 10, pp. 1735-1748.
Wheatley, D.W., Berme,
N., and Ferguson-Pell, M.W., “A low-profile load transducer for monitoring
movement”, Experimental Mechanics, 1980, v. 20, n. 6, pp.19-20.