# Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1610.01683 · HTML: https://mlsanity.com/paper/1610.01683/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2016.

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

- [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)
- [DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimates](https://mlsanity.com/paper/2108.10600/) (2021)
- [U-Sleep's resilience to AASM guidelines](https://mlsanity.com/paper/2209.11173/) (2022)
- [CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities](https://mlsanity.com/paper/2304.06485/) (2023)
- [Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring](https://mlsanity.com/paper/2207.07753/) (2022)
- [SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning](https://mlsanity.com/paper/2209.09452/) (2022)
- [Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2412.12159/) (2024)
- [A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture](https://mlsanity.com/paper/2207.04585/) (2022)
- [MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification](https://mlsanity.com/paper/2501.02949/) (2025)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [EEG-based Image Feature Extraction for Visual Classification using Deep Learning](https://mlsanity.com/paper/2209.13090/) (2022)
- [Feature matching as improved transfer learning technique for wearable EEG](https://mlsanity.com/paper/2201.00644/) (2021)
- [Approximating DTW with a convolutional neural network on EEG data](https://mlsanity.com/paper/2301.12873/) (2023)
- [Hopfield-Enhanced Deep Neural Networks for Artifact-Resilient Brain State Decoding](https://mlsanity.com/paper/2311.03421/) (2023)
- [SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks](https://mlsanity.com/paper/2401.11113/) (2024)
- [Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography](https://mlsanity.com/paper/2412.15947/) (2024)
- [MEASURE: Multi-scale Minimal Sufficient Representation Learning for Domain Generalization in Sleep Staging](https://mlsanity.com/paper/2510.12070/) (2025)
- [Sleep Staging Based on Multi Scale Dual Attention Network](https://mlsanity.com/paper/2107.08442/) (2021)
- [Multimodal Federated Learning under Dual-Axis Modality Missingness](https://mlsanity.com/paper/2608.09240/) (2026)

## Abstract (arXiv)

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold cross-validation. We used class-balanced random sampling within the stochastic gradient descent (SGD) optimization of the CNN to avoid skewed performance in favor of the most represented sleep stages. We achieved high mean F1-score (81%, range 79-83%), mean accuracy across individual sleep stages (82%, range 80-84%) and overall accuracy (74%, range 71-76%) over all subjects. By analyzing and visualizing the filters that our CNN learns, we found that rules learned by the filters correspond to sleep scoring criteria in the American Academy of Sleep Medicine (AASM) manual that human experts follow. Our method's performance is balanced across classes and our results are comparable to state-of-the-art methods with hand-engineered features. We show that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages.