Post on 13-Jan-2016
description
IbPRIA, Póvoa do Varzim, 2009 1
Sleep / Wakefulness from Actigraphy
Pedro Pires1,3, Teresa Paiva2 and João Sanches1,3
1Institute for Systems and Robotics2Faculdade de Medicina da Universidade de Lisboa
3Instituto Superior Técnico
IbPRIA, Póvoa do Varzim, 2009 2
Sleep Disorders Diagnosis
Polysomnography
• tests performed on patients during the sleep to evaluate sleep disorders:
– monitoring of the patient's airflow through the nose and mouth– blood pressure, – heartbeat as measured by an electrocardiograph (ECG)– blood oxygen level– brain wave patterns (EEG)– eye movements (EOG)– movements of respiratory muscles and limbs.
• it is accurate but it is complex and difficult in practice.
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Motivation
A lot of typical sleep disorders involve movement and the actigraphy is a complementary diagnostic tool that may be used to detect these disorders, such as:
• Narcolepsy – the condition of falling asleep spontaneously and unwillingly• Periodic limb movement disorder (PLMD) – sudden involuntary movement
of arm and/or legs during sleep• Circadian rhythm sleep disorder - jet lag and shift work sleep disorder
(SWSD)• Obstructive sleep apnea – the patient can’t get enough deep sleep• Sleepwalking• Sleep paralysis – temporary paralysis of the body shortly before or after
sleep• Delayed sleep syndrome (DSPS) – inability to awaken and fall asleep at
socially acceptable times
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Actigraphy
• Three-axis accelerometer
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Movement characterization
• The purposeness movements during the daytime are intrinsically different from the purposeless nature of the ones during the night.
• Different statistical distributions are associated to each one; Maxwell during the day and Poisson [Gimeno et
al.,1999] during the night
TPM
PnMd trpttrpttrp
;;;where
))(,()())(,()())(,(
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Daytime distribution model
• Acceleration Magnitude
222zyx aaar
xaya
za
r
Maxwell Distribution
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Mixture
MaxwellPoisson (Normal)
ciclo.mpg
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Distribution Mixture
Parameter estimation
),(),()( NNMM cNcnh
L
kkn xpkhn
1
2),()(minarg)(ˆ
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Sleep/Wakefulness (S/W) state
• SW estimation form : )()()( nnnsw
otherwise1
sleepif0)(nSW
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Sleep/Wakefulness (S/W) state estimation
• Binarization with Graph-Cuts
tionRegularizaonBinarizati
)1()())(21)((minarg nn
SWnSWnSWnSWnswSW
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Real data
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Estimated parameters
Exercise
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An insomnia case
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SW
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Conclusions
• Purposeness and purposeless movement components of the human activity
• Different distributions to describe each component: Maxwell for the vigil and Poisson for the sleep state.
• Estimation of the parameters of the mixture, and
• Estimation of the Sleep/Wakefulness state using graph-Cuts