Traditional sleep staging models are difficult to deploy in devices with limited computing power due to high requirements of computational resources. In this study, a lightweight sleep analysis system based on single-channel EEG signals is developed, which deploys a GhostNet-optimized neural network model named GhostSleepNet to assess sleep staging and sleep quality. Users only need to use a brain loop and connect it to this system to achieve sleep staging with high accuracy in a home environment. In this system, convolutional neural networks (CNN) are responsible for extracting higher-order features, GhostNet is designed to maintain the accuracy of CNN extracted features while reducing the parameters of the model to improve the computational efficiency, and gated recurrent unit (GRU) focuses on capturing long-term dependencies and cyclic changes in sleep data. Verification of the five classification tasks on the Sleep-EDF dataset shows that the sleep staging accuracy of GhostSleepNet reaches 84.17%, which is 3%–5% lower than that of traditional sleep staging models. However, the number of FLOPs is only 5 041 111 040, and the computational complexity decreases by 20%–45%, contributing to the development of sleep staging for mobile devices.