Download Signal processing methods in Sleep Research

8/27/2015
Signal processing methods in Sleep
Research
G. Garcia-Molina
1Philips
2
Research NA – Acute Care Solutions Department – Clinical site UW Madison
University of Wisconsin-Madison
Tutorial lecture - EUSIPCO 2015
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EUSIPCO 2015, August 31, 2015
Why do we sleep?
After all from an evolutionary point of view sleep seems like a bad idea,
yet
• Sleep is universal across species.
• Sleep affects the vast majority of body
functions including: immune function,
hormonal regulation, metabolism, and
thermoregulation.
• Sleep’s core function appears to be for
the brain (and by the brain). In
particular for plasticity and memory.
• During sleep, despite the functional
disconnection from the environment,
most neurons remain spontaneously
active at levels similar to wakefulness
Cirelli, C., & Tononi, G. (2008). Is sleep essential? PLoS Biology, 6(8), e216.
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Sleep occupies more than 35% of our time
other activities (19%)
work and work-related (16%)
Sleep
36%
sport (1%)
television (11%)
phone/e-mail (1%)
household activities (8%)
eat/drink (5%)
socializing (3%)
American Time Use Survey, Bureau of Labor Statistics, 2007 annual averages. Calculations based on U.S. life expectancy of 77.8 years (which includes ~ 243,362 hours
of sleep)
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In humans sleep need changes across life
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Watson, N., et al. . (2015). Recommended Amount of Sleep for a Healthy Adult : A Joint Consensus Statement of the
American Academy of Sleep Medicine and Sleep Research Society. SLEEP, 38(6), 843–844.
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Adults should sleep 7 or more
hours per night on a regular
basis.
Sleeping less than 7 hours per
night on a regular basis is
associated with adverse health
outcomes, including weight gain
and obesity, diabetes,
hypertension, heart disease and
stroke, depression, and
increased risk of death.
Sleeping less than 7 hours per
night is also associated with
impaired immune function,
increased pain, impaired
performance, increased errors,
and greater risk of accidents.
Sleeping more than 9 hours per
night on a regular basis may be
appropriate for young adults,
individuals recovering from
sleep debt, and individuals with
illnesses.
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Lack of sleep significantly affects cognitive function
Decreased attention
Impaired working memory
Impaired divergent, innovative thinking
Impaired verbal fluency
Impaired inhibitory control
Increased risk-taking behavior
Impaired humor appreciation
Killgore W, Effects of sleep deprivation on cognition. Prog Brain Res 185: 105-129, 2010
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Theories on the function of
sleep
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The synaptic homeostasis hypothesis (SHY) attempts to explain
why we sleep:
Ø
Plastic processes occurring during wakefulness result in a net increase in
synaptic strength in many brain circuits.
Ø
The role of sleep is to downscale synaptic strength to a baseline level that is
energetically sustainable, makes efficient use of space, and is beneficial for
learning and memory.
Tononi, G., & Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic
and cellular homeostasis to memory consolidation and integration. Neuron,
81(1), 12–34.
Synaptic
potentiating
High synaptic
strength
• Energy costs
• Space costs
• Supply costs
Learning
Synapses
(base strength)
• Energy savings
• Space savings
• Supply savings
Baseline
Wakefulness
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Slow wave
increase
Synaptic
Renormalization
Slow wave
decrease
Sleep
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According to the sequential hypothesis for
memory processing during sleep,
• Memories acquired during wakefulness are processed during sleep in two serial
steps that occur respectively during slow-wave sleep (SWS) and rapid eye
movement (REM) sleep.
• During SWS memories to be retained are distinguished from irrelevant or
competing traces that undergo downgrading or elimination
• Processed memories are stored again during REM sleep which integrates
them with preexisting memories
Giuditta, A. (2014). Sleep memory processing: the sequential hypothesis. Frontiers in
Systems Neuroscience, 8(December), 1–8.
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Recent highlighted research from the University of
Rochester has found that the brain flushes out toxins
during sleep
The cleanup system in the brain, responsible for flushing out toxic
waste products that cells produce with daily use, goes into
overdrive in mice that are asleep. The cells even shrink in size to
make for easier cleaning of the spaces around them.
•
The difference of cerebrospinal fluid influx is seen (through
fluorescent dye ) in the brain of an awake and a sleeping
mouse.
Image created by Katherine
Streeter for NPR
The green represents conversely
restricted flow in the awake animal.
The red represents the
greater flow in a
sleeping animal.
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Xie, L., Kang, H., Xu, Q., Chen, M. J., Liao, Y., Thiyagarajan, M.,
Nedergaard, M. (2013). Sleep drives metabolite clearance from the adult
brain. Science, 342(6156), 373–377.
EUSIPCO 2015, August 31, 2015
Recent highlighted research from the University of Rochester has found
that the brain flushes out toxins during sleep
The cleanup system in the brain, responsible for flushing out toxic
waste products that cells produce with daily use, goes into
overdrive in mice that are asleep. The cells even shrink in size to
make for easier cleaning of the spaces around them.
•
The difference of cerebrospinal fluid influx is seen (through
fluorescent dye ) in the brain of an awake and a sleeping
mouse.
Image created by Katherine
Streeter for NPR
The green represents conversely
restricted flow in the awake animal.
The red represents the
greater flow in a
sleeping animal.
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Xie, L., Kang, H., Xu, Q., Chen, M. J., Liao, Y., Thiyagarajan, M.,
Nedergaard, M. (2013). Sleep drives metabolite clearance from the adult
brain. Science, 342(6156), 373–377.
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Types of sleep: rapid eye
movement (REM) and non
rapid eye movement (NREM)
sleep
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Sleep is a cyclic process where rapid eye movement
(REM) and non-REM sleep stages alternate
Sleep stage
Hypnogram
Deep sleep (N3 sleep)
• NREM (non-REM) sleep is associated with a synchronized brain
activity pattern as opposed to wakefulness.
• REM is distinguished from wakefulness mainly by reduced
responsiveness and muscle atonia.
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Brain centers active during each type of sleep
Brain activity increase
Pontine tegmentum (PT)
Thalamus (Th)
Basal forebrain (BF)
Amygdala (AMY)
Hippocampus HIPPO
Anterior cingulate cortex (ACC)
Occipital area (OA)
Chouchou, F., & Desseilles, M. (2014).
Heart rate variability: a tool to explore the
sleeping brain? Autonomic Neuroscience,
8(December), 1–9.
Brain activity decrease
Thalamus (Th)
Basal ganglia (BG)
Basal forebrain (BF)
Prefrontal cortex (PFC)
Anterior cingulate cortex (ACC)
Precuneus (PC)
Brain activity decrease
Dorsolateral prefrontal (PFC)
Posterior cingulate cortex (PCC)
Interior parietal cortex (IPC)
Brain activity increase
Brainstem centers (BS)
REM
sleep
NREM
sleep
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Heart rate variability to explore the sleeping brain
Reflex loops
- Respiration
- Baroreflex
- Chemoreflex
HR: heart rate
SNS:
Sympathetic
activity
PNS:
parasympathetic
activity
Chouchou, F., & Desseilles, M. (2014). Heart rate variability: a tool to explore the sleeping brain? Autonomic Neuroscience, 8(December), 1–9.
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Slow wave sleep is primarily present in the first part of the sleep episode
Image modified from
Rasch, B., & Born, J. (2013).
About Sleep’s role in Memory.
Physiol Rev, 93, 681–766.
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•
•
•
•
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Sleep is entered through NREM sleep.
NREM and REM sleep alternate with a period of about 90 minutes.
Slow wave sleep predominates in the first third of the night and is linked to the initiation of sleep.
REM sleep predominates in the last 3rd of the night.
Wakefulness in sleep usually accounts for less than 5% of the night.
N1 generally constitutes 2 to 5% of sleep.
N2 generally constitutes 45 to 55% of sleep.
Carskadon, M. A., & Dement, W. C. (2011). Normal human sleep: an
overview. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles
N3 generally constitutes 13 to 23% of sleep.
and practice of sleep medicine (5th ed., pp. 16–26). Elsevier.`
NREM is therefore 75 to 80% of sleep.
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The sleep architecture changes with age
Time [minutes]
WASO: Wake after sleep onset
SWS (N3): Slow wave sleep
From Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV: Meta-analysis of
quantitative sleep parameters from childhood to old age in healthy individuals:
developing normative sleep values across the human lifespan, Sleep 27:1255-1273,
2004
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Markov chains have been applied to model the
sleep stage transitions
Transition graph
Hypnogram
Gray level edges
represent the
sleep stage
transition
• Kim, J., Lee, J.-S., Robinson, P., & Jeong, D.-U. (2009). Markov Analysis of Sleep
Dynamics. Physical Review Letters, 102(17), 1–4.
• Jaaskinen, V. (2009). Modelling Sleep Stages With Markov Chains. Helsinki.
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Polysomnography
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Polysomnography (PSG) provides a rich data source for
understanding sleep and to obtain the hypnogram
Visual inspection of these neurophysiological
signals forms the basis for standard sleep
staging
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Polysomnography (PSG) provides a rich data
source for understanding sleep
Wearables have recently
emerged to monitor sleep
Sleep cycle app
Visual inspection of these neurophysiological
signals forms the basis for standard sleep
staging
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PSG signals exhibit specific patterns
characterizing sleep stages
Low frequency, high
amplitude pattern
High frequency, low
amplitude pattern
Electroencephalogram
Muscle atonia (REM)
Electromyogram
Electrooculogram
Eye blinks
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Rapid eye
movements
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Actigraphy signals (being based on movement monitoring)
can roughly reveal periods of sleep and wakefulness (~57%
accuracy)
Actigraphy
signal
Activity decrease
(indicative of the time
of going to bed)
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Sustained activity increase
(indicative of the waking up time)
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Sleep is by the brain and for the brain: the EEG is the reference
signal to study sleep
The sleeping brain as revealed by
the electroencephalogram (EEG)
High frequency, low amplitude pattern
Wake
REM
200 µV
30 seconds
N1
Sleep slow-waves are present
during deep sleep
200 µV
30 seconds
N2
NREM
sleep
N3 (deep sleep)
Low frequency, high amplitude pattern
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From visualizing a whole night EEG recording, it would
be possible to visually identify NREM sleep
Large amplitude, low frequency regions
200μV
EEG
Wake
Hypnogram
REM
N3
N2
N1
EOG-Left
EOG-Right
1 hour
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Sleep stages
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Wake (eyes closed)
“Alpha” waves (8-12 Hz)
200μV
EEG
Wake
Hypnogram
EOG-Left
EOG-Right
10 seconds
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Wake (eyes closed)
200μV
“Alpha” waves (8-12 Hz)
EEG
Wake
1 second
Power spectrum density
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Alpha activity
Transition from Wake to Sleep
“Alpha” activity (8-12 Hz)
“Theta” activity (4-7 Hz)
200μV
K-complex
EEG
Wake
Hypnogram
N1 (transient stage): 2 to 5% of sleep
“Delta” activity (0.5-4 Hz)
“Spindle” (11-16 Hz)
N2
EOG-Left
EOG-Right
10 seconds
Nine EEG stages characterize sleep onset (next slide)
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Sleep onset can be characterized by nine EEG
stages
Hori, T., Hayashi, M., & Morikawa, T. (1993). Topographical EEG changes and hypnagogic experience. In R. D.
Ogilvie & J. R. Harsh (Eds.), Sleep Onset: Normal and Abnormal Processes (pp. 237–253).
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•
The power in the highfrequency (>8 Hz) range
gradually decreases.
•
The power in the lowfrequency (<6 Hz) range
gradually increases.
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Sleep onset can also characterized by Cardiac
activity changes
Sleep onset was divided into 4 phases in Burgess et al. 1999
Wakefulness
Alpha activity
Mixed α and θ
Theta activity
N2 sleep with arousals
Central electrode
EEG
Stable N2 sleep
Changes in cardiac variables were
analyzed on a beat-by-beat basis
•
•
TWA: T-wave amplitude (µV)
RR duration (b/min)
Occipital electrode
EEG
EMG
Each line
represents a
participant
EOG
ECG
Images modified from:
Burgess, H. J., Kleiman, J., & Trinder, J. (1999). Cardiac activity during sleep onset.
Psychophysiology, 36(3), 298–306.
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K-complexes
N2 sleep
200μV
EEG
N2: 45 to 55% of sleep
Hypnogram
EOG
Left
EOG
Right
10 seconds
• N2: one or more K-complexes
unassociated with arousals
• One or more trains of sleep
spindles
Iber, C.. (2007). The AASM Manual for the Scoring of Sleep and Associated Events:
Rules, Terminology and Technical Specifications. American Academy of Sleep
Medicine (First). American Academy of Sleep Medicine.
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1 second
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N3 sleep
Slow waves
200μV
EEG
Hypnogram
N3
EOG Left
EOG Right
• N3: 20% or more of an epoch consists of
slow wave activity.
Iber, C.. (2007). The AASM Manual for the Scoring of Sleep and
Associated Events: Rules, Terminology and Technical Specifications.
American Academy of Sleep Medicine (First). American Academy of
Sleep Medicine.
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EEG analysis across a whole sleep session
Garcia-Molina, G., Bellesi, M., Pastoor, S., Pfundtner, S., Riedner, B. A., & Tononi, G.
(2013). Online Single EEG Channel Based Automatic Sleep Staging. In D. Harris (Ed.),
Engineering Psychology and Cognitive Ergonomics. Applications and Services (pp.
333–342). Springer Berlin Heidelberg.
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EEG analysis across a whole sleep session
Garcia-Molina, G., Bellesi, M., Pastoor, S., Pfundtner, S., Riedner, B. A., & Tononi, G.
(2013). Online Single EEG Channel Based Automatic Sleep Staging. In D. Harris (Ed.),
Engineering Psychology and Cognitive Ergonomics. Applications and Services (pp.
333–342). Springer Berlin Heidelberg.
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EEG analysis across a whole sleep session
• Amplitude Normalization
• NREM time normalization
Garcia-Molina, G., Bellesi, M., Pastoor, S., Pfundtner, S., Riedner, B. A., & Tononi, G.
(2013). Online Single EEG Channel Based Automatic Sleep Staging. In D. Harris (Ed.),
Engineering Psychology and Cognitive Ergonomics. Applications and Services (pp.
333–342). Springer Berlin Heidelberg.
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Simultaneous power time-courses for the beta, sigma and clocklike delta frequency bands in the first NREM episode: in terms of
neural activity
The number of neurons (N) in
each mode beta, sigma and
delta, is expressed as a
percentage of the number of
neurons in beta mode (N0) at
the start of the NREM episode.
Merica, H., & Fortune, R. D. (2004). State transitions
between wake and sleep, and within the ultradian
cycle, with focus on the link to neuronal activity.
Sleep Medicine Reviews, 8(6), 473–85.
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Whole-night averages for NREM delta EEG power are significantly
correlated with HF-HRV (high frequency heart-rate variability)
EEG
ECG
Delta power
estimation
R-R detection
+ spectral
analysis
0
25
50
75
N1 (norm. time %)
100
25
50
75
N2 (norm. time %)
100
25
50
75
N3 (norm. time %)
100
Image modified from:
Rothenberger, S. D.,et al. (2014). Time-varying correlations between delta EEG power and heart rate variability in midlife
women: The SWAN Sleep Study. Psychophysiology, 52(2015), 572–584.
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Automatic sleep staging
Macro analysis of sleep
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Automatic sleep staging
PSG signals
Preprocessing
• Artifact
rejection
• Artifact
correction
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Sleep stage
Feature
extraction
• Linear
features
• Nonlinear
• Time /
Frequency
Feature
classification
• Linear
• Nonlinear
classifier
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Artifacts: Artifacts are unwanted patterns
not caused by the underlying physiological
events of interest
Non biological artifacts
Biological artifacts
• Noise from the power line
(50Hz EU/60 Hz US)
• Ocular artifacts, e.g. eye blinks, eye
movements
• Electrical equipment
• Muscular activity (mainly from head
and facial muscles)
• Movement
• Electrode pops
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• Sweat
• EKG/pulse spikes in EEG
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Movement artifact
Power line artifact
High power line noise !
EEG
EOG
Left
EOG
Right
30 seconds
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Power line artifact
• Any fault in the
recording circuitry
can be seen as a 60
Hz artifact
From the SLEEP 2015 tutorial: “Catch the wave”
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Sweat artifact (electrodermal artifact)
EEG
Hypnogram
N3
EOG Left
EOG Right
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Sweat artifact (electrodermal artifact): HP
filtered signal
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The electrodermal artifact occurs more frequently
during NREM sleep (N2, N3)
Sano, A., & Picard, R. W. (1968). Quantitative Analysis of Electrodermal Activity
during Sleep. International Journal of Psychophysiology, 94(3), 1968.
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EKG artifact in the EEG
1 second
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Pulse artifact
Movement artifact
Pulse artifact
N2
10 seconds
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Frequency-analysis based features
δ: 0.5 – 4.5 Hz
θ: 4.0 – 7.0 Hz
α: 8.0 – 12 Hz
σ: 11 – 15 Hz
β: 15 – 30 Hz
δ
θ
alpha
σ
beta
Power spectrum density for Wake,
NREM and REM stages.
Radha, M., Garcia-Molina, G., Poel, M., & Tononi, G. (2014). Comparison of Feature and Classifier Algorithms for Online Automatic Sleep Staging Based on
a Single EEG Signal. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1876–1880).
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Time-analysis based features
Radha, M., Garcia-Molina, G., Poel, M., & Tononi, G. (2014). Comparison of Feature and Classifier Algorithms for Online Automatic Sleep Staging Based on
a Single EEG Signal. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1876–1880).
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Nonlinear analysis based features
Nx
•
Time and frequency domain entropy
H mi = - å p( xi ) log( p( xi ))
i =1
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•
Higuchi’s fractal dimension (HD)
•
Lempel-Ziv complexity (LC)
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Machine learning methods and evaluation
Signal
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Illustration: the meaning of kappa in the
context of automatic sleep staging
Reference (manually scored)
Automatically scored (kappa = 0.4)
Reference (manually scored)
Automatically scored (kappa = 0.6)
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Micro analysis of sleep
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Analysis of sleep microevents
• Spindles
• Slow-waves
Zygierewicz, J., Blinowska, K. J., Durka, P. J., Szelenberger, W.,
Niemcewicz, S., & Androsiuk, W. (1999). High resolution study of sleep
spindles. Clinical Neurophysiology : Official Journal of the International
Federation of Clinical Neurophysiology, 110(12), 2136–47.
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Sleep microevents play a role in memory
consolidation
Rasch, B., & Born, J. (2013). About Sleep’s role in Memory. Physiol Rev, 93, 681–766.
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Slow waves: detection
Signal from frontal electrode
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Detecting EEG events that have
three elements:
1) a negative-going zerocrossing,
2) 2) a negative peak with
absolute amplitude of
at least 30 microvolts,
and
3) a positive going zerocrossing which occurs
between 100 and 900
milliseconds after the
detected negativegoing zero-crossing.
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Slow waves: analysis over multiple EEG channels
and wave propagation properties
Slow waves
Detected across
several channels +
sorting
Massimini, M., Huber, R., Ferrarelli, F., Hill, S., &
Tononi, G. (2004). The sleep slow oscillation as a
traveling wave. The Journal of Neuroscience, 24(31),
6862–6870.
Riedner et al., 2012
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Slow waves: analysis over multiple EEG channels
and wave propagation properties
EEG Traces
Topography
Riedner, B. A., Hulse, B. K., Murphy, M. J., Ferrarelli,
F., & Tononi, G. (2011). Temporal dynamics of cortical
sources underlying spontaneous and peripherally
evoked slow waves. In Progress in Brain Research
(Vol. 193, pp. 201–218).
Trajectories
Speed
Riedner et al., 2012
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Slow wave activity (EEG power in the 0.5 to 4 Hz) is
a marker of sleep need
680 µV2
SWA 120 µV2
10000
100
slow-wave activity (mV2)
0
Fz
Cz
P4
8000
800
Fz
Cz
P4
6000
600
4000
40
0
2000
20
0
0
0
2
4
6
Time [h]
59
8
Huber,
unpublished
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Sleep spindle detection
Andrillon, T., Nir, Y., Staba, R. J., Ferrarelli, F., Cirelli,
C., Tononi, G., & Fried, I. (2011). Sleep spindles in
humans: insights from intracranial EEG and unit
recordings. The Journal of Neuroscience, 31(49),
17821–17834.
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The rate of spindles appears to predict sleep
stability in the presence of noise
Dang-Vu, T. T., McKinney, S. M., Buxton, O. M., Solet,
J. M., & Ellenbogen, J. M. (2010). Spontaneous brain
rhythms predict sleep stability in the face of noise.
Current Biology : CB, 20(15), R626–7.
But sleep spindles seem to also be related to: learning
•
Gais, S., Mölle, M., Helms, K., & Born, J. (2002). Learning-dependent increases in sleep spindle
density. The Journal of Neuroscience, 22(15), 6830–6834.
Reduced sleep spindle activity is present in schizophrenia
• Ferrarelli, F., Huber, R., Peterson, M. J., Massimini, M., Murphy, M., Riedner, B. a, … Tononi, G.
(2007). Reduced sleep spindle activity in schizophrenia patients. The American Journal of
Psychiatry, 164(3), 483–492.
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Models of sleep regulation
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Models of sleep regulation: two process (S-C)
model
The longer one is awake, the higher the need for sleep is.
S can be modelled as saturating exponentials
Sleep need depends on time of the day
C can be roughly modeled as a
sinusoidal process
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Borbély, A. A. (1982). A two process model of sleep
regulation. Human Neurobiology, 1(3), 195–204.
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Consequences of total sleep deprivation
Instead of sleeping
Borbély, A. A. (1982). A two process model of sleep
regulation. Human Neurobiology, 1(3), 195–204.
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Modeling the “S” process with SWA
Sleep need builds up during wakefulness and dissipates during sleep. According to the
two process model, the instantaneous rate of decrease in sleep need is related to the
SWA during NREM sleep by a proportionality constant γ
dS (t )
= -g × SWA(t )
dt
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(1)
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Modeling the “S” process with SWA
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Modeling the “S” process with SWA
•
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The parameters γ and S0 are experimentally estimated using the information
from the manually scored sleep hypnogram and the discretization (see Equation
3) of Equation (2).
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Example of the application of the model to
several nights from the same participant
SWA and sleep-need dissipation
2
SWA [mV ]
1000
500
0
0
100
200
300
SWA + sleep-need
estimation
2
SWA [mV ]
1000
500
100
200
300
400
2
SWA [mV ]
1000
68
500
0
0
100
200
300
Time [min]
400
500
1000
SWA [mV2]
0
0
500
0
0
100
200
Time [min]
300
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Conclusive remarks
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Conclusive remarks
• Sleep can only be understood from a holistic perspective involving:
– Sleep architecture
– Sleep microevents
– Sleep need dissipation
– Circadian rhythm
• In all these steps, signal processing plays a key role.
– Sleep architecture can be (should be) automatically estimated from PSG signals.
– Sleep microevents (slow-waves and spindles) provide critical information on sleep
restoration and memory processing. These events can only be detected automatically
through signal processing algorithms.
– Modelling of the two processes helps in understanding the effects of sleep
deprivation and recovery.
70
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Trends where signal processing is key
• Real-time sleep staging
• Unobtrusive monitoring of sleep.
– Actigraphy,
– Wrist PPG
– Wearable EEG/EOG
– Radar/video (touchless)- monitoring
• High density EEG analysis
71
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36