# L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging

arXiv: https://arxiv.org/abs/2301.03441 · HTML: https://mlsanity.com/paper/2301.03441/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 56 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [What is being transferred in transfer learning?](https://mlsanity.com/paper/2008.11687/) (2020)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging](https://mlsanity.com/paper/1809.10932/) (2018)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification](https://mlsanity.com/paper/2105.11043/) (2021)
- [SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach](https://mlsanity.com/paper/1903.02108/) (2019)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [Recurrent Batch Normalization](https://mlsanity.com/paper/1603.09025/) (2016)
- [Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging](https://mlsanity.com/paper/1911.03221/) (2019)
- [SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging](https://mlsanity.com/paper/2105.13864/) (2021)
- [Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning](https://mlsanity.com/paper/1907.13177/) (2019)
- [DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates](https://mlsanity.com/paper/2108.10600/) (2021)
- [RobustSleepNet: Transfer learning for automated sleep staging at scale](https://mlsanity.com/paper/2101.02452/) (2021)
- [Mixed Neural Network Approach for Temporal Sleep Stage Classification](https://mlsanity.com/paper/1610.06421/) (2016)
- [Automatic sleep stage classification with deep residual networks in a mixed-cohort setting](https://mlsanity.com/paper/2008.09416/) (2020)
- [TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging](https://mlsanity.com/paper/2203.12590/) (2022)
- [Feature matching as improved transfer learning technique for wearable EEG](https://mlsanity.com/paper/2201.00644/) (2021)

## Built on by (most cited first, 21 of 21)

- [Self-supervised Learning for Electroencephalogram: A Systematic Survey](https://mlsanity.com/paper/2401.05446/) (2024)
- [Brant-X: A Unified Physiological Signal Alignment Framework](https://mlsanity.com/paper/2409.00122/) (2024)
- [CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities](https://mlsanity.com/paper/2304.06485/) (2023)
- [SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models](https://mlsanity.com/paper/2506.08574/) (2025)
- [S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models](https://mlsanity.com/paper/2310.06715/) (2023)
- [Structure-Preserving Transformers for Sequences of SPD Matrices](https://mlsanity.com/paper/2309.07579/) (2023)
- [sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals](https://mlsanity.com/paper/2602.13857/) (2026)
- [SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages](https://mlsanity.com/paper/2307.05362/) (2023)
- [Assessing the importance of long-range correlations for deep-learning-based sleep staging](https://mlsanity.com/paper/2402.17779/) (2024)
- [Evaluating the Influence of Temporal Context on Automatic Mouse Sleep Staging through the Application of Human Models](https://mlsanity.com/paper/2406.16911/) (2024)
- [PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging](https://mlsanity.com/paper/2503.04582/) (2025)
- [Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography](https://mlsanity.com/paper/2605.02245/) (2026)
- [NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals](https://mlsanity.com/paper/2511.03488/) (2025)
- [Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging](https://mlsanity.com/paper/2605.09905/) (2026)
- [Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks](https://mlsanity.com/paper/2410.19819/) (2024)
- [MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification](https://mlsanity.com/paper/2502.17470/) (2025)
- [Fully-automated sleep staging: multicenter validation of a generalizable deep neural network for Parkinson's disease and isolated REM sleep behavior disorder](https://mlsanity.com/paper/2602.09793/) (2026)
- [Staging by the Book: Automatic Sleep Stage Classification Using Scoring Rules](https://mlsanity.com/paper/2605.22859/) (2026)
- [Efficient Temporal Modeling for Mobile Sleep Staging via Lightweight Random Attention](https://mlsanity.com/paper/2606.13694/) (2026)
- [Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets](https://mlsanity.com/paper/2607.04934/) (2026)
- [Rethinking PPG-based Sleep Staging: Datasets, Metrics, and Benchmarks](https://mlsanity.com/paper/2608.00943/) (2026)

## Abstract (arXiv)

Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal dependency in the sleep data. Yet, exploring this long-term dependency when developing sleep staging models has remained untouched. In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose. We thus introduce a method for efficient long sequence modelling and propose a new deep learning model, L-SeqSleepNet, which takes into account whole-cycle sleep information for sleep staging. Evaluating L-SeqSleepNet on four distinct databases of various sizes, we demonstrate state-of-the-art performance obtained by the model over three different EEG setups, including scalp EEG in conventional Polysomnography (PSG), in-ear EEG, and around-the-ear EEG (cEEGrid), even with a single EEG channel input. Our analyses also show that L-SeqSleepNet is able to alleviate the predominance of N2 sleep (the major class in terms of classification) to bring down errors in other sleep stages. Moreover the network becomes much more robust, meaning that for all subjects where the baseline method had exceptionally poor performance, their performance are improved significantly. Finally, the computation time only grows at a sub-linear rate when the sequence length increases.