The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
Open accessAbstract
Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http://github.com/pquochuy/SeqSleepNet). At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modeling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modeling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms the state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.
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Van den Bulcke, L., Peeters, A.-M., Heremans, E., Davidoff, H., Borzée, P., De Vos, M., Emsell, L., Van den Stock, J., De Roo, M., Tournoy, J., et al. (2023) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.10778 · Cited by 23
A Feature Selection Method for Multimodal Multispectral LiDAR Sensing
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Metadata
- Authors
- Phan, H., Andreotti, F., Cooray, N., Chen, O. Y., & De Vos, M.
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2019-03
- Volume
- 27
- Issue
- 3
- Pages
- 400-410
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2893892260
- Cited by
- 623
- References
- 55
- FWCI
- 27.45
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging. https://app.cochat.ai/papers/10.1109/tnsre.2019.2896659
- The paper
- Phan, H., Andreotti, F., Cooray, N., Chen, O. Y., & De Vos, M. (2019). SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/tnsre.2019.2896659
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