# Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal

arXiv: https://arxiv.org/abs/2103.16215 · HTML: https://mlsanity.com/paper/2103.16215/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 67 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition](https://mlsanity.com/paper/1603.01249/) (2016)
- [Classification of Two-channel Signals by Means of Genetic Programming](https://mlsanity.com/paper/1904.05027/) (2019)

## Built on by (most cited first, 6 of 6)

- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [Ensemble of Convolution Neural Networks on Heterogeneous Signals for Sleep Stage Scoring](https://mlsanity.com/paper/2107.11045/) (2021)
- [Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging](https://mlsanity.com/paper/2312.03196/) (2023)
- [Deep Learning for Sleep Stages Classification: Modified Rectified Linear Unit Activation Function and Modified Orthogonal Weight Initialisation](https://mlsanity.com/paper/2203.04371/) (2022)
- [LGFNet: A CTC-Guided Local-Global Fusion Framework for Single-Channel Sleep Staging](https://mlsanity.com/paper/2607.25197/) (2026)
- [SWINSleepNet: A Hierarchical Context-Aware Framework for Sleep Staging (v2)](https://mlsanity.com/paper/2608.02183/) (2026)

## Abstract (arXiv)

Sleeping problems have become one of the major diseases all over the world. To tackle this issue, the basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. After its recording, the specialists have to score the different signals according to one of the standard guidelines. This process is carried out manually, which can be highly time-consuming and very prone to annotation errors. Therefore, over the years, many approaches have been explored in an attempt to support the specialists in this task. In this paper, an approach based on convolutional neural networks is presented, where an in-depth comparison is performed in order to determine the convenience of using more than one signal simultaneously as input. Additionally, the models were also used as parts of an ensemble model to check whether any useful information can be extracted from signal processing a single signal at a time which the dual-signal model cannot identify. Tests have been performed by using a well-known dataset called expanded sleep-EDF, which is the most commonly used dataset as the benchmark for this problem. The tests were carried out with a leave-one-out cross-validation over the patients, which ensures that there is no possible contamination between training and testing. The resulting proposal is a network smaller than previously published ones, but which overcomes the results of any previous models on the same dataset. The best result shows an accuracy of 92.67% and a Cohen's Kappa value over 0.84 compared to human experts.