Classification of Sleep EEGClassification of Sleep EEGVVáclav Gerla áclav Gerla (gerlav@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

Stages of Sleep, HypnogramStages of Sleep, Hypnogram1. Wake (wakefulness, waking stage)2. REM (Rapid Eye Movements) // dreams3. NREM 1 (shallow/drowsy sleep)4. NREM 2 (light sleep)5. NREM 3 (deepening sleep)6. NREM 4 (deepest sleep)

Hypnogram:

SSleep Disordersleep DisordersHeadachesInsomnia (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 apneaREM sleep disorders

Proportion of REM/NREM stagesProportion of REM/NREM stages

0

5

10

15

20

25

30

35

40

3 18 40 70

REMNREM(3+4)

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 LaboratoryMeasuring 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 exampleEEG signal example19 EEG signals, EKG signal (+50 Hz artifact)

Brain Wave FrequenciesBrain Wave FrequenciesDelta (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

BBinaural Beat inaural Beat FrequenciesFrequenciesExample of frequencies: // sporadic

0.15-0.3 Hz - depression4.5-6.5 Hz - wakeful dreaming, vivid images4-8 Hz - dreaming sleep, deep meditation, subconscious mind5.0-10.0 Hz - relaxation5.8 Hz - dizziness7 Hz - increased reaction time7.83 Hz - earth resonance8.6-9.8 Hz - induces sleep, tingling sensations15.0-18.0 Hz - increased mental ability18 Hz - significant improvements in memory55 Hz - Tantric yoga

LEFT EAR – 70HzRIGHT EAR – 74Hz

→ Binaural Beat 4Hz

Brain Wave Generator: http://www.BWgen.com

Stage WakeStage 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 REMStage REM

EEG: - relatively low voltage- mixed frequency

EOG: - contains rapid eye movements

EMG: - tonically suppressed (Sleep Paralysis)

Stage NREM 1Stage NREM 1(shallow/drowsy sleep)(shallow/drowsy sleep)

EEG: - the absence of alpha activity - Vertex sharp waves

EOG: - slow eye movement

EMG: - relatively lower amplitude

Stage NREM 2Stage NREM 2 (light sleep)(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 3Stage NREM 3 (deepening sleep)(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 4Stage NREM 4 (deepest sleep)(deepest sleep)

As NREM 3 + delta activity duration more than 50% for epoch

ArtifactsArtifacts

Other artifacts:

Muscle 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 StructureSystem 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 ExtractionFeature ExtractionHypnogram (rate by expert)

1Hz

29 Hz

……

……

……

……

……

……

……

……

….

Power spectral

density

EEG (Fpz-Cz)

EEG (Pz-Oz)

Spectrogram:

Feature NormalizationFeature NormalizationThe features contain great number of peaks

-> normalization

NREM4 stage detection: Wake stage detection:

Decision RulesDecision RulesSearching 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:

EEG 16-30Hz > 20%

EEG 0.5-3Hz > 85%

EEG 0.5-3Hz > 65%

WAKE

S4

S3

EEG 13-15Hz < 15%and

EOG 0.15-1.2Hz > 50%

EEG 13-15Hz > 20%

REM

S2

EEG 13-15Hz > 10% S1

true

false

true

false

Markov models Markov models (utilization of time-dependence)(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

ResultsResults- Final classification accuracy approximately 80% - Problem with detection S1 stage