1 Classification of Sleep EEG_GERLA

7/27/2019 1 Classification of Sleep EEG_GERLA

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Classification of Sleep EEGVclav Gerla ([email protected])

Gerstner laboratory, Department of CyberneticsTechnick 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
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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Stages of Sleep, Hypnogram

1. 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:


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Sleep DisordersHeadaches

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

0

5

10

15

20

25

30

3540

3 18 40 70

REM

NREM(3+4)

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 example19 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 - depression4.5-6.5 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 EAR70Hz

RIGHT EAR74Hz

Binaural Beat 4Hz

Brain Wave Generator: http://www.BWgen.com
http://www.bwgen.com/http://www.bwgen.com/http://www.bwgen.com/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 (>=75uV)

- slow delta activity (


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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

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)


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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 ExtractionHypnogram (rate by expert)

1Hz

29 Hz

.

Powerspectraldensity

EEG (Fpz-Cz)

EEG (Pz-Oz)

Spectrogram:


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Feature Normalization

The features contain

great number of peaks

-> normalization

NREM4 stage detection: Wake stage detection:


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Decision 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
http://www.cs.waikato.ac.nz/ml/wekahttp://www.cs.waikato.ac.nz/ml/weka


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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 transitionsprobability


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Results

- Final classification accuracy approximately 80%

- Problem with detection S1 stage

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1 Classification of Sleep EEG_GERLA

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