# SLEEPNET: Automated Sleep Staging System via Deep Learning

arXiv: https://arxiv.org/abs/1707.08262 · HTML: https://mlsanity.com/paper/1707.08262/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2017.

## Built on by (most cited first, 10 of 10)

- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [Self-supervised EEG Representation Learning for Automatic Sleep Staging](https://mlsanity.com/paper/2110.15278/) (2021)
- [Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks](https://mlsanity.com/paper/2202.07679/) (2022)
- [Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel Digital Biomarkers for Sleep Health](https://mlsanity.com/paper/2407.04753/) (2024)
- [Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics](https://mlsanity.com/paper/2502.17213/) (2025)
- [Feature matching as improved transfer learning technique for wearable EEG](https://mlsanity.com/paper/2201.00644/) (2021)
- [SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features](https://mlsanity.com/paper/2209.11174/) (2022)
- [Toward Cohort Intelligence: A Universal Cohort Representation Learning Framework for Electronic Health Record Analysis](https://mlsanity.com/paper/2304.04468/) (2023)
- [Performance and utility trade-off in interpretable sleep staging](https://mlsanity.com/paper/2211.03282/) (2022)
- [sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging](https://mlsanity.com/paper/2501.16329/) (2025)

## Abstract (arXiv)

Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect 50-70 million adults in the United States (Hillman et al., 2006). Overnight polysomnography (PSG), including brain monitoring using electroencephalography (EEG), is a central component of the diagnostic evaluation for sleep disorders. While PSG is conventionally performed by trained technologists, the recent rise of powerful neural network learning algorithms combined with large physiological datasets offers the possibility of automation, potentially making expert-level sleep analysis more widely available. We propose SLEEPNET (Sleep EEG neural network), a deployed annotation tool for sleep staging. SLEEPNET uses a deep recurrent neural network trained on the largest sleep physiology database assembled to date, consisting of PSGs from over 10,000 patients from the Massachusetts General Hospital (MGH) Sleep Laboratory. SLEEPNET achieves human-level annotation performance on an independent test set of 1,000 EEGs, with an average accuracy of 85.76% and algorithm-expert inter-rater agreement (IRA) of kappa = 79.46%, comparable to expert-expert IRA.