# Inter-database validation of a deep learning approach for automatic sleep scoring

arXiv: https://arxiv.org/abs/2009.10365 · HTML: https://mlsanity.com/paper/2009.10365/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2020.

## Built on by (most cited first, 30 of 38)

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- [aSAGA: Automatic Sleep Analysis with Gray Areas](https://mlsanity.com/paper/2310.02032/) (2023)
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- [Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout](https://mlsanity.com/paper/2409.01962/) (2024)
- [Multi-task deep-learning for sleep event detection and stage classification](https://mlsanity.com/paper/2501.09519/) (2025)
- [StableSleep: Source-Free Test-Time Adaptation for Sleep Staging with Lightweight Safety Rails](https://mlsanity.com/paper/2509.02982/) (2025)
- [PRISM: Exploring Heterogeneous Pretrained EEG Foundation Model Transfer to Clinical Differential Diagnosis](https://mlsanity.com/paper/2603.02268/) (2026)
- [NeuroNarrator: A Generalist EEG-to-Text Foundation Model for Clinical Interpretation via Spectro-Spatial Grounding and Temporal State-Space Reasoning](https://mlsanity.com/paper/2603.16880/) (2026)
- [PanLUNA: An Efficient and Robust Query-Unified Multimodal Model for Edge Biosignal Intelligence](https://mlsanity.com/paper/2604.04297/) (2026)
- [Towards Multi-Source Domain Generalization for Sleep Staging with Noisy Labels](https://mlsanity.com/paper/2604.10009/) (2026)
- [Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence](https://mlsanity.com/paper/2604.10404/) (2026)
- [Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs](https://mlsanity.com/paper/2605.18172/) (2026)

## Abstract (arXiv)

In this work we describe a new deep learning approach for automatic sleep staging, and carry out its validation by addressing its generalization capabilities on a wide range of sleep staging databases. Prediction capabilities are evaluated in the context of independent local and external generalization scenarios. Effectively, by comparing both procedures it is possible to better extrapolate the expected performance of the method on the general reference task of sleep staging, regardless of data from a specific database. In addition, we examine the suitability of a novel approach based on the use of an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. Validation results show good general performance, as compared to the expected levels of human expert agreement, as well as state-of-the-art automatic sleep staging approaches