Evaluation of the sleep quality based on bed sensor signals: Time ariant analysis

Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM -Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive modeling (TVAM) and Wavelet Decomposition (WD) and b) the performance of K-Nearest Neighbor (KNN) and Feed Forward Neural Networks (FFNN) classifiers. In the current analysis, 17 full polysomnography recordings from healthy subjects were used. The best agreement for Wake- NREM-REM with respect to the gold standard was 71.95 ± 7.47%25 of accuracy and 0.42 ± 0.10 of kappa index for TVAMLD while WD-FFNN shows 67.17 ± 11.88%25 of accuracy and 0.39 ± 0.13 of kappa index. The results suggest that the sleep quality assessment out of sleep centers could be possible and as consequence more people could be beneficiated. © 2010 IEEE.

Automatic Detection; Autoregressive modeling; Current analysis; Gold standards; Healthy subjects; K-nearest neighbors; Kappa index; Macrostructures; Non-rapid eye movements; Polysomnography; Rapid eye movement; Sensor signals; Sleep quality; Time variant; Eye movements; Neural networks; Rating; Sensors; Sleep research; Wakes; Wavelet decomposition; Quality control; article; automation; feasibility study; human; polysomnography; sleep; Automation; Feasibility Studies; Humans; Polysomnography; Sleep

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