# Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy

arXiv: https://arxiv.org/abs/1710.02094 · HTML: https://mlsanity.com/paper/1710.02094/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2017.

## Built on by (most cited first, 30 of 41)

- [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)
- [RobustSleepNet: Transfer learning for automated sleep staging at scale](https://mlsanity.com/paper/2101.02452/) (2021)
- [Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers](https://mlsanity.com/paper/2208.06991/) (2022)
- [FedDTG:Federated Data-Free Knowledge Distillation via Three-Player Generative Adversarial Networks](https://mlsanity.com/paper/2201.03169/) (2022)
- [U-Sleep's resilience to AASM guidelines](https://mlsanity.com/paper/2209.11173/) (2022)
- [SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models](https://mlsanity.com/paper/2506.08574/) (2025)
- [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)
- [S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models](https://mlsanity.com/paper/2310.06715/) (2023)
- [Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity](https://mlsanity.com/paper/2206.01922/) (2022)
- [MSED: a multi-modal sleep event detection model for clinical sleep analysis](https://mlsanity.com/paper/2101.02530/) (2021)
- [Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring](https://mlsanity.com/paper/2207.01910/) (2022)
- [A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture](https://mlsanity.com/paper/2207.04585/) (2022)
- [Enhancing Healthcare with EOG: A Novel Approach to Sleep Stage Classification](https://mlsanity.com/paper/2310.03757/) (2023)
- [Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel Digital Biomarkers for Sleep Health](https://mlsanity.com/paper/2407.04753/) (2024)
- [Leveraging Generic Time Series Foundation Models for EEG Classification](https://mlsanity.com/paper/2510.27522/) (2025)
- [Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Models](https://mlsanity.com/paper/2512.09591/) (2025)
- [What Do EEG Foundation Models Capture from Human Brain Signals?](https://mlsanity.com/paper/2605.11410/) (2026)
- [A Systematic Review on Sleep Stage Classification and Sleep Disorder Detection Using Artificial Intelligence](https://mlsanity.com/paper/2405.11008/) (2024)
- [PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging](https://mlsanity.com/paper/2503.04582/) (2025)
- [SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features](https://mlsanity.com/paper/2209.11174/) (2022)
- [aSAGA: Automatic Sleep Analysis with Gray Areas](https://mlsanity.com/paper/2310.02032/) (2023)
- [Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review](https://mlsanity.com/paper/2312.14996/) (2023)
- [NAPS: Attention-Based Fusion of Heterogeneous Physiological Signals](https://mlsanity.com/paper/2511.03488/) (2025)
- [AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts](https://mlsanity.com/paper/2512.14461/) (2025)
- [Performance and utility trade-off in interpretable sleep staging](https://mlsanity.com/paper/2211.03282/) (2022)
- [Convolutional Monge Mapping Normalization for learning on sleep data](https://mlsanity.com/paper/2305.18831/) (2023)

## Abstract (arXiv)

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph - a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 instead of 30 second scoring epochs. A T1N marker based on unusual sleep-stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.