Automatic sleep stage classification using two facial electrodes.
Standard sleep stage classification is based on visual analysis of central EEG, EOG and EMG signals. Automatic analysis with a reduced number of sensors has been studied as an easy alternative to the standard. In this study, a single-channel electro-oculography (EOG) algorithm was developed for separation of wakefulness, SREM, light sleep (S1, S2) and slow wave sleep (S3, S4). The algorithm was developed and tested with 296 subjects. Additional validation was performed on 16 subjects using a low weight single-channel Alive Monitor. In the validation study, subjects attached the disposable EOG electrodes themselves at home. In separating the four stages total agreement (and Cohen's Kappa) in the training data set was 74% (0.59), in the testing data set 73% (0.59) and in the validation data set 74% (0.59). Self-applicable electro-oculography with only two facial electrodes was found to provide reasonable sleep stage information.
Virkkala
Jussi
J
Sleep Laboratory, Brain and Work Research Center, Finnish Institute of Occupational Health, Helsinki, Finland. jussi.virkkala@ttl.fi
Velin
Riitta
R
Himanen
Sari-Leena
SL
Värri
Alpo
A
Müller
Kiti
K
Hasan
Joel
J
D000328:Adult; D000465:Algorithms; D001698:Biomedical Engineering; D003663:Decision Trees; D004566:Electrodes; D004585:Electrooculography / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D005145:Face; D006801:Humans; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages / Q000502:physiology*; D055815:Young Adult