# Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

arXiv: https://arxiv.org/abs/1907.13177 · HTML: https://mlsanity.com/paper/1907.13177/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

## Built on by (most cited first, 30 of 31)

- [SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification](https://mlsanity.com/paper/2105.11043/) (2021)
- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [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)
- [L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging](https://mlsanity.com/paper/2301.03441/) (2023)
- [ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training](https://mlsanity.com/paper/2107.04470/) (2021)
- [Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation](https://mlsanity.com/paper/2210.06286/) (2022)
- [Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers](https://mlsanity.com/paper/2208.06991/) (2022)
- [U-Sleep's resilience to AASM guidelines](https://mlsanity.com/paper/2209.11173/) (2022)
- [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)
- [PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations](https://mlsanity.com/paper/2302.10182/) (2023)
- [A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture](https://mlsanity.com/paper/2207.04585/) (2022)
- [Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones](https://mlsanity.com/paper/2103.09171/) (2021)
- [Pediatric Automatic Sleep Staging: A comparative study of state-of-the-art deep learning methods](https://mlsanity.com/paper/2108.10211/) (2021)
- [PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation](https://mlsanity.com/paper/2506.11170/) (2025)
- [Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography](https://mlsanity.com/paper/2605.02245/) (2026)
- [Feature matching as improved transfer learning technique for wearable EEG](https://mlsanity.com/paper/2201.00644/) (2021)
- [A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data](https://mlsanity.com/paper/2211.02638/) (2022)
- [Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models](https://mlsanity.com/paper/2304.06033/) (2023)
- [aSAGA: Automatic Sleep Analysis with Gray Areas](https://mlsanity.com/paper/2310.02032/) (2023)
- [Label-Efficient Sleep Staging Using Transformers Pre-trained with Position Prediction](https://mlsanity.com/paper/2404.15308/) (2024)
- [SelectiveFinetuning: Enhancing Transfer Learning in Sleep Staging through Selective Domain Alignment](https://mlsanity.com/paper/2501.03764/) (2025)
- [AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts](https://mlsanity.com/paper/2512.14461/) (2025)
- [Sleep Staging Based on Multi Scale Dual Attention Network](https://mlsanity.com/paper/2107.08442/) (2021)
- [EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning](https://mlsanity.com/paper/2205.10356/) (2022)
- [U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging](https://mlsanity.com/paper/2306.04663/) (2023)
- [MorpheusNet: Resource efficient sleep stage classifier for embedded on-line systems](https://mlsanity.com/paper/2401.10284/) (2024)
- [Personalized Sleep Prediction via Deep Adaptive Spatiotemporal Modeling and Sparse Data](https://mlsanity.com/paper/2509.09018/) (2025)
- [Combining scEEG and PPG for reliable sleep staging](https://mlsanity.com/paper/2602.15042/) (2026)

## Abstract (arXiv)

Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging. Methods: We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain (i.e. the small cohort) to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database. The target domains are purposely adopted to cover different degrees of data mismatch to the source domains. Results: Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach. Conclusions: These results suggest the efficacy of the proposed approach in addressing the above-mentioned data-variability and data-inefficiency issues. Significance: As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small. The source code and the pretrained models are available at http://github.com/pquochuy/sleep_transfer_learning.