Classification of Sleep EEG
Václav Gerla (gerlav@fel.cvut.cz)
Gerstner laboratory, Department of Cybernetics
Technická 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
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:
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
Proportion of REM/NREM stages
%
40
REM
NREM(3+4)
35
30
25
20
15
10
5
0
3
18
40
70
age (years)
The decrease of NREM sleeping is caused partially by decrease of delta waves.
(does not meet criteria for delta waves)
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.
EEG signal example
19 EEG signals, EKG signal (+50 Hz artifact)
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
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
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
Stage REM
EEG:
- relatively low voltage
- mixed frequency
EOG:
- contains rapid eye movements
EMG:
- tonically suppressed (Sleep Paralysis)
Stage NREM 1(shallow/drowsy sleep)
EEG:
- the absence of alpha activity
- Vertex sharp waves
EOG:
- slow eye movement
EMG:
- relatively lower amplitude
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
Stage NREM 3 (deepening sleep)
EEG:
- consists of high-voltage (>=75uV)
- slow delta activity (<=2 Hz) // electrodes Fpz-Cz or Pz-Oz
EOG:
- the absence eye movement
- delta waves from EEG
EMG:
- low tonic activities
Stage NREM 4 (deepest sleep)
As NREM 3 + delta activity duration more than 50% for epoch
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)
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
Feature Extraction
Hypnogram (rate by expert)
EEG (Fpz-Cz)
Spectrogram:
Power spectral density
…………………………………………….
EEG (Pz-Oz)
1Hz
29 Hz
Feature Normalization
The features contain
great number of peaks
-> normalization
NREM4 stage detection:
Wake stage detection:
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 datapreprocessing, classification, regression, clustering,
association rules and visualization…
The most significant found rules:
EEG 16-30Hz > 20%
WAKE
true
EEG 0.5-3Hz > 85%
S4
false
EEG 0.5-3Hz > 65%
S3
EEG 13-15Hz < 15%
and
EOG 0.15-1.2Hz > 50%
EEG 13-15Hz > 20%
REM
true
S2
EEG 13-15Hz > 10%
S1
false
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
Results
- Final classification accuracy approximately 80%
- Problem with detection S1 stage