mlsanityData to 14 Oct 2026

Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasets

arXiv 2607.04934 ↗First posted 6 Jul 2026cs.LGcited by 0

On the map: State-space sequence models (S4, Mamba) → Physiological signal modeling

Abstract (arXiv)

Automatic sleep staging is a key technology for precise diagnosis and treatment of sleep disorders as well as long-term home sleep monitoring. Portable electroencephalogram (EEG) devices have become the focus of research due to their convenience in data collection. However, current methods still face three major challenges: large parameter sizes that easily lead to overfitting on small datasets, low accuracy in classifying difficult stages such as N1 and REM, unclear optimal training dataset size, and difficulty in deployment. This paper proposes GamSleepNet, a lightweight and low-latency automatic sleep staging framework for single-channel EEG. The framework features the FEB module, which combines improved Gabor kernels with learnable filters for feature extraction, uses the Mamba architecture to build a temporal classification network, introduces a novel contrastive loss and a two-stage training strategy, and experimentally validates the optimal dataset size for single-channel EEG sleep staging models. On the Sleepedf dataset, this model achieves an overall accuracy of 87.86 percent with only 30.86 thousand parameters, with all metrics reaching SOTA levels and significantly improving the identification accuracy of challenging sleep stages.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Physiological signal modeling, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 19 of its 38 references that are in the map. First posted on arXiv 6 Jul 2026.

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19 of 38 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,718
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  4. 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,336
  5. 2017DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGcited by 157
  6. 2018SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Stagingcited by 91
  7. 2026Mamba-3: Improved Sequence Modeling using State Space Principlescited by 89
  8. 2019U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Stagingcited by 88
  9. 2020XSleepNet: Multi-View Sequential Model for Automatic Sleep Stagingcited by 88
  10. 2021SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantificationcited by 84
  11. 2017A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriescited by 58
  12. 2019SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approachcited by 54
7 more
  1. 2017Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsycited by 41
  2. 2021Self-supervised Contrastive Learning for EEG-based Sleep Stagingcited by 27
  3. 2021DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimatescited by 26
  4. 2023L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Stagingcited by 21
  5. 2022SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learningcited by 7
  6. 2019Naive Gabor Networks for Hyperspectral Image Classificationcited by 5
  7. 2022A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecturecited by 4

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  1. 2024BiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging
  2. 2026LightSleepX: A Lightweight, Inception-Based Dual-Modal Network for Sleep Staging
  3. 2024Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography
  4. 2024NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
  5. 2021A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging
  6. 2024A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input
  7. 2025Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution
  8. 2025sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging