An Interpretable and Efficient Sleep Staging Algorithm: DetectsleepNet

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.