Sleep architecture measurement based on cardiorespiratory parameters.
This paper presents a method for the detection of wakeful state, rapid eye movement sleep (REM), light sleep (N1&N2) and deep sleep (N3&N4) based on cardiorespiratory parameters. Experiments were conducted with data of 625 subjects without sleep-disordered breathing selected from the SHHS dataset. Compared to previous studies, our method considers results of neighboring epochs classification and epoch position over record time. The method demonstrates Cohen's kappa of 0.57 ± 0.13 and the accuracy of 71.4 ± 8.6 %. The results might contribute to the development of screening tools for diagnostics, prevention, and management of sleep disorders.
Alexander
A
Tataraidze
Lyudmila
L
Korostovtseva
Lesya
L
Anishchenko
Mikhail
M
Bochkarev
Yurii
Y
Sviryaev
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D017286:Polysomnography / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D012891:Sleep Apnea Syndromes / Q000503:physiopathology; D012894:Sleep Stages / Q000502:physiology*; D012895:Sleep, REM; D014851:Wakefulness