TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Gaiduk, Maksym
A1  - Perea Rodríguez, Juan José
A1  - Seepold, Ralf
A1  - Martínez Madrid, Natividad
A1  - Penzel, Thomas
A1  - Glos, Martin
A1  - Ortega, Juan Antonio
T1  - Estimation of sleep stages analyzing respiratory and movement signals
JF  - IEEE journal of biomedical and health informatics
N2  - The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.
KW  - biomedical signal processing
KW  - regression analysis
KW  - sleep stages
KW  - sleep study
KW  - heart rate variability
KW  - sleep apnea
KW  - probability
Y1  - 2022
SN  - 2168-2194
SS  - 2168-2194
U6  - https://doi.org/10.1109/JBHI.2021.3099295
DO  - https://doi.org/10.1109/JBHI.2021.3099295
VL  - 26
IS  - 2
SP  - 505
EP  - 514
S1  - 10
PB  - IEEE
CY  - New York
ER  -