# SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages

arXiv: https://arxiv.org/abs/2307.05362 · HTML: https://mlsanity.com/paper/2307.05362/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 56 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach](https://mlsanity.com/paper/1903.02108/) (2019)
- [EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals](https://mlsanity.com/paper/1806.01875/) (2018)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification](https://mlsanity.com/paper/1805.06546/) (2018)
- [SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging](https://mlsanity.com/paper/2105.13864/) (2021)
- [DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates](https://mlsanity.com/paper/2108.10600/) (2021)
- [L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging](https://mlsanity.com/paper/2301.03441/) (2023)
- [TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging](https://mlsanity.com/paper/2203.12590/) (2022)
- [Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models](https://mlsanity.com/paper/2304.06033/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Staging](https://mlsanity.com/paper/2512.22976/) (2025)
- [Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions](https://mlsanity.com/paper/2603.12296/) (2026)

## Abstract (arXiv)

Deep neural networks have played an important role in automatic sleep stage classification because of their strong representation and in-model feature transformation abilities. However, class imbalance and individual heterogeneity which typically exist in raw EEG signals of sleep data can significantly affect the classification performance of any machine learning algorithms. To solve these two problems, this paper develops a generative adversarial network (GAN)-powered ensemble deep learning model, named SleepEGAN, for the imbalanced classification of sleep stages. To alleviate class imbalance, we propose a new GAN (called EGAN) architecture adapted to the features of EEG signals for data augmentation. The generated samples for the minority classes are used in the training process. In addition, we design a cost-free ensemble learning strategy to reduce the model estimation variance caused by the heterogeneity between the validation and test sets, so as to enhance the accuracy and robustness of prediction performance. We show that the proposed method can improve classification accuracy compared to several existing state-of-the-art methods using three public sleep datasets.