title: Sleep stage classification based on bioradiolocation signals.
abstract: This paper presents an algorithm for the detection of wakeful state, rapid eye movement sleep (REM) and non-REM sleep based on the analysis of respiratory movements acquired through a bioradar. We used the data from 29 subjects without sleep-related breathing disorders who underwent a polysomnography study at a sleep laboratory. A leave-one-subject-out cross-validation procedure was used for testing the classification performance. Cohen's kappa of 0.56 ± 0.16 and accuracy of 75.13 ± 9.81 % were achieved when compared to polysomnography results. The results of our work contribute to the development of home sleep monitoring systems.
authors.forename: Alexander
authors.initials: A
authors.lastname: Tataraidze
authors.forename: Lesya
authors.initials: L
authors.lastname: Anishchenko
authors.forename: Lyudmila
authors.initials: L
authors.lastname: Korostovtseva
authors.forename: Bert Jan
authors.initials: BJ
authors.lastname: Kooij
authors.forename: Mikhail
authors.initials: M
authors.lastname: Bochkarev
authors.forename: Yurii
authors.initials: Y
authors.lastname: Sviryaev
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography / Q000295:instrumentation / Q000379:methods*; D011825:Radar / Q000295:instrumentation; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D012890:Sleep / Q000502:physiology; D012894:Sleep Stages / Q000502:physiology*; D012895:Sleep, REM / Q000502:physiology; D014851:Wakefulness / Q000502:physiology; D055815:Young Adult