The human body is more fragile than people think. In order to survive, it requires sleep just as much as food, water, or oxygen. This is a basic principle of human physiology that has been borne out by thousands of research studies.In general, sleep is a state where our bodies and minds rest and rejuvenate (Spriggs 2009). It is obligatory for our normal physiological, mental and emotional functioning during awake hours. The belief that it is possible to have just a couple of hours of sleep a night over a long period of time without suffering any negative consequences is a common misconception (National Heart Lung and Blood Institute (NHLBI) 2011). It is categorically beyond doubt that when sleep contains even slight abnormalities, the aftermath can lead to physical illness, psychological problems or an untimely death (Lee-Chiong et al 2012).A popular misconception is that adults have to sleep at least 7-8 h every night to be rejuvenated properly, while children require far more hours of sleep (National Heart Lung and Blood Institute (NHLBI) 2011). However, this is only standard recommended advice; sleep requirements are individual for every person (National Heart Lung and Blood Institute (NHLBI) 2011). In addition, getting many hours of sleep does not always guarantee a healthy and rested state, because the crucial point here is not the quantity, but the quality (National Heart Lung and Blood Institute (NHLBI) 2011).

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 nonobtrusive 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.

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