Measuring Sleep in the Laboratory

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Classification of Sleep EEGVáclav Gerla
(gerlav_at_fel.cvut.cz)Gerstner laboratory,
Department of CyberneticsTechnická 2, 166 27
Prague, Czech Republic Faculty of Electrical
Engineering, Czech Technical University in Prague
- Stages of Sleep - Sleep Disorders - Measuring
Sleep in the Laboratory - Brain Wave
Frequencies - Artifacts - Sleep stages analysis
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Stages of Sleep, Hypnogram
1. Wake (wakefulness, waking stage) 2. REM (Rapid
Eye Movements) // dreams 3. NREM 1
(shallow/drowsy sleep) 4. NREM 2 (light sleep) 5.
NREM 3 (deepening sleep) 6. NREM 4 (deepest
sleep) Hypnogram
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Sleep Disorders
Headaches Insomnia (sleep - -) - difficulty
falling asleep - waking up frequently during the
night - waking up too early in the morning -
unrefreshing sleep Sleepiness (sleep ) -
fall asleep while driving - concentrating at
work, school, or home - have difficulty
remembering Restless Legs Syndrome - sensations
of discomfort in the legs during periods of
inactivity Narcolepsy - sudden and
irresistible onsets of sleep during normal waking
hours Sleep apnea REM sleep disorders
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Proportion of REM/NREM stages

age (years)
The decrease of NREM sleeping is caused partially
by decrease of delta waves. (does not meet
criteria for delta waves)
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Measuring Sleep in the Laboratory
Electroencephalogram (EEG) Measures electrical
activity of the brain. Electrooculogram (EOG)
Measures eye movements. An electrode placed near
the eye will record a change in voltage as the
eye moves. Electromyogram (EMG) Measures
electrical activity of the muscles. In humans,
sleep researchers usually record from under the
chin, as this area undergoes dramatic changes
during sleep.
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EEG signal example
19 EEG signals, EKG signal (50 Hz artifact)
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Brain Wave Frequencies
Delta (0.1 to 3 Hz) deep / dreamless sleep,
non-REM sleep Theta (4-8 Hz) connection with
creativity, intuition, daydreaming,
fantasizing Alpha (8-12 Hz) relaxation, mental
work - thinking or calculating Beta (above 12
Hz) normal rhythm, absent or reduced in areas of
cortical damage
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Binaural Beat Frequencies

• Example of frequencies // sporadic
• 0.15-0.3 Hz - depression
• 4.5-6.5 Hz - wakeful dreaming, vivid images
• 4-8 Hz - dreaming sleep, deep meditation,
  subconscious mind
• 5.0-10.0 Hz - relaxation
• 5.8 Hz - dizziness
• 7 Hz - increased reaction time
• 7.83 Hz - earth resonance
• 8.6-9.8 Hz - induces sleep, tingling sensations
• 15.0-18.0 Hz - increased mental ability
• 18 Hz - significant improvements in memory
• 55 Hz - Tantric yoga
• LEFT EAR 70Hz
• RIGHT EAR 74Hz
• Binaural Beat 4Hz
• Brain Wave Generator http//www.BWgen.com

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Stage Wake
EEG - rhythmic alpha waves (8-12Hz) // only if
the eyes are closed - beta waves
(20-30Hz) EOG - eye movement (observation
process) EMG - continual tonically activity of
muscles
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Stage REM
EEG - relatively low voltage - mixed
frequency EOG - contains rapid eye
movements EMG - tonically suppressed (Sleep
Paralysis)
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Stage NREM 1(shallow/drowsy sleep)
EEG - the absence of alpha activity -
Vertex sharp waves EOG - slow eye
movement EMG - relatively lower amplitude
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Stage NREM 2 (light sleep)
EEG - sleep spindles (oscillating with the
frequency between 12-15 Hz) - K-complexes (high
voltage, sharp rising and sharp falling wave)
- relatively low voltage mixed
frequency EOG - the absence eye
movements EMG - constant tonic activity
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Stage NREM 3 (deepening sleep)
EEG - consists of high-voltage (gt75uV) - slow
delta activity (lt2 Hz) // electrodes Fpz-Cz or
Pz-Oz EOG - the absence eye movement - delta
waves from EEG EMG - low tonic activities
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Stage NREM 4 (deepest sleep)
As NREM 3 delta activity duration more than 50
for epoch
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Artifacts
Muscle artifacts
Other artifacts
- Eye Flutter, slow and rapid eye movements -
ECG artifact - Sweat artifact - Metal contact
(touching metal during recording) - Salt Bridge
(between two electrodes) - Static electricity
artifact - Glossokinetic (movements of tongue)
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System Structure
reduce data quantity (speeds up total computing
time)
divide signal into 1 second segments
compute mean power density in individual
frequency bands for each segment
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Feature Extraction
Hypnogram (rate by expert)
1Hz
EEG (Fpz-Cz)
.
Power spectral density
EEG (Pz-Oz)
Spectrogram
29 Hz
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Feature Normalization
The features contain great number of peaks -gt
normalization
NREM4 stage detection
Wake stage detection
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Decision Rules
Searching suitable decision rules - convert all
features of all patients to the Weka format. -
Weka (http//www.cs.waikato.ac.nz/ml/weka) is a
collection of machine learning algorithmus
contains tools for data-preprocessing,
classification, regression, clustering,
association rules and visualization The most
significant found rules
true
false
true
false
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Markov models (utilization of time-dependence)
Aplication to segments which - all rules are
false - more rules are true
Markov models use - contextual information in
EEG signa - approximate knowledge of
transitions probability
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Results

• Final classification accuracy approximately 80
• Problem with detection S1 stage