  Sleep quality directly impacts human health and quality of life, so accurate
sleep staging is essential for assessing sleep quality. However, most
traditional methods are inefficient and time-consuming due to segmenting
different sleep cycles by manual labeling. In contrast, automated sleep staging
technology not only directly assesses sleep quality but also helps sleep
specialists analyze sleep status, significantly improving efficiency and
reducing the cost of sleep monitoring, especially for continuous sleep
monitoring. Most of the existing models, however, are deficient in
computational efficiency, lightweight design, and model interpretability. In
this paper, we propose a neural network architecture based on the prior
knowledge of sleep experts. Specifically, 1) Propose an end-to-end model named
DetectsleepNet that uses single-channel EEG signals without additional data
processing, which has achieved an impressive 80.9% accuracy on the SHHS dataset
and an outstanding 88.0% accuracy on the Physio2018 dataset. 2) Constructure an
efficient lightweight sleep staging model named DetectsleepNet-tiny based on
DetectsleepNet, which has just 6% of the parameter numbers of existing models,
but its accuracy exceeds 99% of state-of-the-art models, 3) Introducing a
specific inference header to assess the attention given to a specific EEG
segment in each sleep frame, enhancing the transparency in the decisions of
models. Our model comprises fewer parameters compared to existing ones and
ulteriorly explores the interpretability of the model to facilitate its
application in healthcare. The code is available at
https://github.com/komdec/DetectSleepNet.git.
