# Multi-task deep-learning for sleep event detection and stage classification

arXiv: https://arxiv.org/abs/2501.09519 · HTML: https://mlsanity.com/paper/2501.09519/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [Inter-database validation of a deep learning approach for automatic sleep scoring](https://mlsanity.com/paper/2009.10365/) (2020)
- [Automatic sleep stage classification with deep residual networks in a mixed-cohort setting](https://mlsanity.com/paper/2008.09416/) (2020)
- [DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal](https://mlsanity.com/paper/1812.04079/) (2018)
- [MSED: a multi-modal sleep event detection model for clinical sleep analysis](https://mlsanity.com/paper/2101.02530/) (2021)
- [Multi-Task Learning for Arousal and Sleep Stage Detection Using Fully Convolutional Networks](https://mlsanity.com/paper/2406.01834/) (2024)
- [Automatic Detection of Cortical Arousals in Sleep and their Contribution to Daytime Sleepiness](https://mlsanity.com/paper/1906.01700/) (2019)

## Abstract (arXiv)

Polysomnographic sleep analysis is the standard clinical method to accurately diagnose and treat sleep disorders. It is an intricate process which involves the manual identification, classification, and location of multiple sleep event patterns. This is complex, for which identification of different types of events involves focusing on different subsets of signals, resulting on an iterative time-consuming process entailing several visual analysis passes. In this paper we propose a multi-task deep-learning approach for the simultaneous detection of sleep events and hypnogram construction in one single pass. Taking as reference state-of-the-art methodology for object-detection in the field of Computer Vision, we reformulate the problem for the analysis of multi-variate time sequences, and more specifically for pattern detection in the sleep analysis scenario. We investigate the performance of the resulting method in identifying different assembly combinations of EEG arousals, respiratory events (apneas and hypopneas) and sleep stages, also considering different input signal montage configurations. Furthermore, we evaluate our approach using two independent datasets, assessing true-generalization effects involving local and external validation scenarios. Based on our results, we analyze and discuss our method's capabilities and its potential wide-range applicability across different settings and datasets.