Slow wave sleep (SWS) and rapid eye movement (REM) are two of the most important sleep stages that are 4 considered in many studies. Detection of these two sleep stages will help researchers in many applications to detect sleep-5 related diseases and disorders and also in many fields of neuroscience studies such as cognitive impairment and memory 6 consolidation. Since manual sleep staging is time-consuming, subjective, and expensive; designing an efficient automatic 7 sleep scoring system will

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... overcome some of these difficulties. Many studies have proposed automatic sleep staging systems 8 with different methods. In recent years, deep learning methods show their potential in different applications. In this 9 study, we propose SWS and REM detection system by using a deep neural network. In the proposed system we use a 10 kernel-based layer to get the system closer to the manual scoring approach. Also, we use a new method for augmenting 11 EEG signals to prevent over-fitting the network. The results show the efficiency of the designed system in SWS and 12 REM detection. 13