Classification of Sleep EEG Václav Gerla (gerlav@fel. cvut 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 % 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: - 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)

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) 1Hz ……………………………………………. EEG (Fpz-Cz) 1Hz ……………………………………………. Power spectral density EEG (Pz-Oz) Spectrogram: 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 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 (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