A.B. Tataraidze Post-graduate Student, Junior Research Scientist, Bauman Moscow State Technical Uni-versity. E-mail: tataraidze@rslab.ru L.N. Anishchenko Ph.D. (Eng.), Associate Professor, Senior Research Scientist, Bauman Moscow State Technical University. E-mail: anishchenko@rslab.ru L.S. Korostovtseva Ph.D.(Med.), Research Scientist, Almazov Federal North-West Medical Research Centre. M. V. Bochkarev Ph.D. (Med.), Junior Research Scientist, Almazov Federal North-West Medical Research Centre. Yu.V. Sviryaev Dr.Sc. (Med.), Head of the Somnology Group, Almazov Federal North-West Medical Re-search Centre. S.I. Ivashov Ph.D.(Eng.), Head of the Remote Sensing Laboratory, Bauman Moscow State Technical Uni-versity. V. S. Vereten Ph.D. (Phys.-Mat.), Associate Professor, Plekhanov Russian University of Economics, Moscow A.S. Bugaev Academician RAS, Principal Research Scientist, Bauman Moscow State Technical University.

Objectives: Permanent non-contact monitoring of sleep at home can help to control and to diagnose sleep disorders early, to improve sleep quality and to determine the best time to wake up. One of the most perspective non-contact methods is bioradiolocation (BRL). BRL is a remote sensing technique allowing to perform non-contact vital signs monitoring of living objects on the base of analysis of specific biometric modulation in reflected radiolocation signal. Two research tasks should be solved for the development of a sleep monitor - sleep stages classification and detection of breathing pauses. This study is devoted to the first one. Material and methods: We used the data from 29 subjects (mean age of 45.38±15.71 years) without sleep-related breathing disorders who underwent a PSG study at a sleep laboratory due to suspected sleep disorders. Full-night PSG and BRL monitoring were simultaneously performed. PSG records were scored by a physician according to AASM scoring rules. Wake/REM/NREM classification was performed by bagging classifier and a set of 23 features extracted from BRL signals. Moreover, a few simple heuristics based on knowledge of normal sleep were utilized to improve classification performance. A leave-one-subject-out cross-validation procedure was used for testing the classification performance. Results: The accuracy of 72.62±8.91% and Cohen\'s kappa of 0.49±0.12 were achieved for the classifier. Using heuristics we increased the accuracy 75.13±9.81% and the kappa to of 0.56±0.16. Conclusions: We have shown that based on bioradiolocation fairly accurate sleep structure detection is possible, and this technology appears to be promising.

 

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