DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG
Open accessAbstract
This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared with the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.
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The small and large temporal filters in the MST block should be able to learn high and low-frequency patterns from the input, respectively.
“The small and large temporal filters should be able to learn high and low-frequency patterns from the input, respectively (Supratak et al., 2017).”
From A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision (2021) · cites this paper as Supratak et al., 2017
Representation Learning (p. 2) says DeepSleepNet uses small and large filters, but describes the small filters as better at capturing when EEG patterns appear and the large filters as better at capturing frequency components. Model Specification (p. 3) repeats that distinction. Neither passage specifies high-frequency patterns for small filters or low-frequency patterns for large ones. (LLM, conf=0.84)
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“The small and large temporal filters should be able to learn high and low-frequency patterns from the input, respectively (Supratak et al., 2017).”
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- Authors
- Supratak, A., Dong, H., Wu, C., & Guo, Y.
- Affiliations
- Imperial College London (GB)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2017-11
- Volume
- 25
- Issue
- 11
- Pages
- 1998-2008
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2604096629
- Cited by
- 1,376
- References
- 36
- Citations by year
- 2026: 169 · 2025: 206 · 2024: 191 · 2023: 216 · 2022: 185 · 2021: 145 · 2020: 121 · 2019: 89 · 2018: 50 · 2017: 4
- FWCI
- 41.46
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- single-channel EEG, bidirectional LSTM, convolutional neural networks, EEG feature extraction, Sleep-EDF, Mass, cross-dataset generalization
- MeSH terms
- Machine Learning, Adult, Algorithms, Automation, Electroencephalography, Female, Humans, Male, Models, Neurological, Sleep Stages, Reproducibility of Results, Neural Networks, Computer, Polysomnography, Healthy Volunteers
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