TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Gaiduk, Maksym
A1  - Penzel, Thomas
A1  - Ortega, Juan Antonio
A1  - Seepold, Ralf
T1  - Automatic sleep stages classification using respiratory, heart rate and movement signals
JF  - Physiological Measurement
N2  - Objective: This paper presents an algorithm for non-invasive sleep stage identification using respiratory, heart rate and movement signals. The algorithm is part of a system suitable for long-term monitoring in a home environment, which should support experts analysing sleep. Approach: As there is a strong correlation between bio-vital signals and sleep stages, multinomial logistic regression was chosen for categorical distribution of sleep stages. Several derived parameters of three signals (respiratory, heart rate and movement) are input for the proposed method. Sleep recordings of five subjects were used for the training of a machine learning model and 30 overnight recordings collected from 30 individuals with about 27 000 epochs of 30 s intervals each were evaluated. Main results: The achieved rate of accuracy is 72% for Wake, NREM, REM (with Cohen's kappa value 0.67) and 58% for Wake, Light (N1 and N2), Deep (N3) and REM stages (Cohen's kappa is 0.50). Our approach has confirmed the potential of this method and disclosed several ways for its improvement. Significance: The results indicate that respiratory, heart rate and movement signals can be used for sleep studies with a reasonable level of accuracy. These inputs can be obtained in a non-invasive way applying it in a home environment. The proposed system introduces a convenient approach for a long-term monitoring system which could support sleep laboratories. The algorithm which was developed allows for an easy adjustment of input parameters that depend on available signals and for this reason could also be used with various hardware systems.
KW  - Sleep stage classification
KW  - Multinomial logistic regression
KW  - Non-invasive sleep study
KW  - Heart rate
KW  - Sleep study
Y1  - 2018
SN  - 0967-3334
SS  - 0967-3334
U6  - https://doi.org/10.1088/1361-6579/aaf5d4
DO  - https://doi.org/10.1088/1361-6579/aaf5d4
VL  - 39
IS  - 12
SP  - 14
S1  - 14
ER  -