# SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

arXiv: https://arxiv.org/abs/1809.10932 · HTML: https://mlsanity.com/paper/1809.10932/

## Key facts

- Cited by 91 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2018.

## Built on by (most cited first, 30 of 91)

- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification](https://mlsanity.com/paper/2105.11043/) (2021)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals](https://mlsanity.com/paper/2405.17766/) (2024)
- [Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification](https://mlsanity.com/paper/2109.01824/) (2021)
- [SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging](https://mlsanity.com/paper/2105.13864/) (2021)
- [RobustSleepNet: Transfer learning for automated sleep staging at scale](https://mlsanity.com/paper/2101.02452/) (2021)
- [L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging](https://mlsanity.com/paper/2301.03441/) (2023)
- [EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification](https://mlsanity.com/paper/2407.20254/) (2024)
- [ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training](https://mlsanity.com/paper/2107.04470/) (2021)
- [Generalizable Sleep Staging via Multi-Level Domain Alignment](https://mlsanity.com/paper/2401.05363/) (2023)
- [Robust learning from corrupted EEG with dynamic spatial filtering](https://mlsanity.com/paper/2105.12916/) (2021)
- [Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation](https://mlsanity.com/paper/2210.06286/) (2022)
- [Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers](https://mlsanity.com/paper/2208.06991/) (2022)
- [U-Sleep's resilience to AASM guidelines](https://mlsanity.com/paper/2209.11173/) (2022)
- [Deep Efficient Continuous Manifold Learning for Time Series Modeling](https://mlsanity.com/paper/2112.03379/) (2021)
- [CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities](https://mlsanity.com/paper/2304.06485/) (2023)
- [DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCI](https://mlsanity.com/paper/2601.06134/) (2026)
- [TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging](https://mlsanity.com/paper/2203.12590/) (2022)
- [Automated Sleep Staging via Parallel Frequency-Cut Attention](https://mlsanity.com/paper/2204.03173/) (2022)
- [SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning](https://mlsanity.com/paper/2209.09452/) (2022)
- [S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models](https://mlsanity.com/paper/2310.06715/) (2023)
- [NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces](https://mlsanity.com/paper/2605.14698/) (2026)
- [SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging](https://mlsanity.com/paper/2110.09966/) (2021)
- [Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model](https://mlsanity.com/paper/2211.15374/) (2022)
- [CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversion](https://mlsanity.com/paper/2311.07788/) (2023)
- [RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG](https://mlsanity.com/paper/2401.05411/) (2023)
- [MSED: a multi-modal sleep event detection model for clinical sleep analysis](https://mlsanity.com/paper/2101.02530/) (2021)
- [Avoiding Post-Processing with Event-Based Detection in Biomedical Signals](https://mlsanity.com/paper/2209.11007/) (2022)
- [PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition](https://mlsanity.com/paper/2209.13645/) (2022)

## Abstract (arXiv)

Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography (PSG) epochs one at a time. In this work, 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. 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 modelling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling 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 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.