1. Classification of Sleep EEG Václav Gerla (gerlav@fel. cvut
Classification of Sleep EEG Václav Gerla Gerstner laboratory, Department of Cybernetics Technická 2, 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

2. 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:

3. 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

4. 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)

5. 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.

6. EEG signal example
19 EEG signals, EKG signal (+50 Hz artifact)

7. 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

8. Binaural Beat Frequencies
Example of frequencies: // sporadic
Hz - depression
Hz - wakeful dreaming, vivid images
4-8 Hz - dreaming sleep, deep meditation, subconscious mind
Hz - relaxation
5.8 Hz - dizziness
7 Hz - increased reaction time
7.83 Hz - earth resonance
Hz - induces sleep, tingling sensations
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:

9. 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

10. Stage REM EEG: - relatively low voltage - mixed frequency
EOG: - contains rapid eye movements
EMG: - tonically suppressed (Sleep Paralysis)

11. Stage NREM 1(shallow/drowsy sleep)
EEG: - the absence of alpha activity
- Vertex sharp waves
EOG: - slow eye movement
EMG: - relatively lower amplitude

12. Stage NREM 2 (light sleep)
EEG: - sleep spindles (oscillating with the frequency between 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

13. 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

14. Stage NREM 4 (deepest sleep)
As NREM 3 + delta activity duration more than 50% for epoch

15. Artifacts Muscle artifacts: Other artifacts: - ECG artifact
- 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)

16. 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

17. Feature Extraction Hypnogram (rate by expert) 1Hz …………………………………………….
EEG (Fpz-Cz)
1Hz
…………………………………………….
Power spectral density
EEG (Pz-Oz)
Spectrogram:
29 Hz

18. Feature Normalization
The features contain great number of peaks
-> normalization
NREM4 stage detection:
Wake stage detection:

19. Decision Rules Searching suitable decision rules:
- convert all features of all patients to the Weka format.
- 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:
EEG 16-30Hz > 20%
EEG 0.5-3Hz > 85%
EEG 0.5-3Hz > 65%
WAKE S4 S3
EEG 13-15Hz < 15%
and
EOG Hz > 50%
EEG 13-15Hz > 20%
REM S2
EEG 13-15Hz > 10% S1
true
false
true
false

20. 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

21. Results Final classification accuracy approximately 80%
Problem with detection S1 stage