ArticleInternational journal of environmental research and public health2022
CAttSleepNet: Automatic End-to-End Sleep Staging Using Attention-Based Deep Neural Networks on Single-Channel EEG.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for EEG-based sleep stage classification: a review.Medical & biological engineering & computing · 2026Pooled it
- Influence of Channel Selection and Subject's Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms.Sensors (Basel, Switzerland) · 2023Article
- Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing.Frontiers in sleep · 2023Review
- DLKN-MLC: A Disease Prediction Model via Multi-Label Learning.International journal of environmental research and public health · 2022Article
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Authors and funding
6 authors.
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Abstract
Accurate sleep staging results can be used to measure sleep quality, providing a reliable basis for the prevention and diagnosis of sleep-related diseases. The key to sleep staging is the feature representation of EEG signals. Existing approaches rarely consider local features in feature extraction, and fail to distinguish the importance of critical and non-critical local features. We propose an innovative model for automatic sleep staging with single-channel EEG, named CAttSleepNet. We add an attention module to the convolutional neural network (CNN) that can learn the weights of local sequences of EEG signals by exploiting intra-epoch contextual information. Then, a two-layer bidirectional-Long Short-Term Memory (Bi-LSTM) is used to encode the global correlations of successive epochs. Therefore, the feature representations of EEG signals are enhanced by both local and global context correlation. Experimental results achieved on two real-world sleep datasets indicate that the CAttSleepNet model outperforms existing models. Moreover, ablation experiments demonstrate the validity of our proposed attention module.
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