Enhancing Sleep Stage Classification Performance in AttFuseNet Based on Attention Mechanisms and Decision Fusion
Year: 2025
Type: conference-paper
Abstract: Sleep quality is closely related to individual health. Sleep staging results can effectively measure individual sleep quality. Automatic sleep staging has been extensively studied. In this study, we propose an enhanced deep learning model called AttFuseNet. Based on the original CNN-LSTM model, channel and spatial attention mechanism module are fused respectively to further improve the performance of sleep staging, and decision fusion technology is added at the end to correct errors that may be caused by a single classifier. The model was tested on three publicly available datasets and the results showed improvements on several evaluation metrics. In addition, our experimental results also confirm that the model has a good ability to generalize between different datasets.
Authors: Shuang Zhang, Qin Wei
Institution: Wuhan University of Technology
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
OpenAlex ID: W4413008255