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

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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