# Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

arXiv: https://arxiv.org/abs/2008.09416 · HTML: https://mlsanity.com/paper/2008.09416/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [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)
- [L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging](https://mlsanity.com/paper/2301.03441/) (2023)
- [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)
- [Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoring](https://mlsanity.com/paper/2207.01910/) (2022)
- [sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals](https://mlsanity.com/paper/2602.13857/) (2026)
- [Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Models](https://mlsanity.com/paper/2512.09591/) (2025)
- [Pediatric Automatic Sleep Staging: A comparative study of state-of-the-art deep learning methods](https://mlsanity.com/paper/2108.10211/) (2021)
- [Approximating DTW with a convolutional neural network on EEG data](https://mlsanity.com/paper/2301.12873/) (2023)
- [aSAGA: Automatic Sleep Analysis with Gray Areas](https://mlsanity.com/paper/2310.02032/) (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)
- [Multi-task deep-learning for sleep event detection and stage classification](https://mlsanity.com/paper/2501.09519/) (2025)
- [From Sleep Staging to Spindle Detection: A Case Study on End-to-End Automated Sleep Analysis](https://mlsanity.com/paper/2505.05371/) (2025)
- [On Sleep-Wake Disorder Identification from Polysomnographic Representations](https://mlsanity.com/paper/2610.17451/) (2026)

## Abstract (arXiv)

Study Objectives: Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few small-scale databases for developing models, and generalizability to new datasets is thus unknown. We investigated a novel deep neural network to assess the generalizability of several large-scale cohorts. Methods: A deep neural network model was developed using 15684 polysomnography studies from five different cohorts. We applied four different scenarios: 1) impact of varying time-scales in the model; 2) performance of a single cohort on other cohorts of smaller, greater or equal size relative to the performance of other cohorts on a single cohort; 3) varying the fraction of mixed-cohort training data compared to using single-origin data; and 4) comparing models trained on combinations of data from 2, 3, and 4 cohorts. Results: Overall classification accuracy improved with increasing fractions of training data (0.25%: 0.782 ± 0.097, 95% CI [0.777-0.787]; 100%: 0.869 ± 0.064, 95% CI [0.864-0.872]), and with increasing number of data sources (2: 0.788 ± 0.102, 95% CI [0.787-0.790]; 3: 0.808 ± 0.092, 95% CI [0.807-0.810]; 4: 0.821 ± 0.085, 95% CI [0.819-0.823]). Different cohorts show varying levels of generalization to other cohorts. Conclusions: Automatic sleep stage scoring systems based on deep learning algorithms should consider as much data as possible from as many sources available to ensure proper generalization. Public datasets for benchmarking should be made available for future research.