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Classification of Sleep EEG Václav Gerla Classification of Sleep EEG Václav Gerla Gerstner laboratory, Department of Cybernetics Technická.

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Presentation on theme: "Classification of Sleep EEG Václav Gerla Classification of Sleep EEG Václav Gerla Gerstner laboratory, Department of Cybernetics Technická."— Presentation transcript:

1 Classification of Sleep EEG Václav Gerla Classification of Sleep EEG Václav Gerla Gerstner laboratory, Department of Cybernetics Technická 2, Prague, Czech Republic Faculty of Electrical Engineering, Czech Technical University in - 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 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)

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 29 Hz ……………………………………………. Power spectral density EEG (Fpz-Cz) EEG (Pz-Oz) Spectrogram:

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 (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…http://www.cs.waikato.ac.nz/ml/weka 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


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