# RED: Deep Recurrent Neural Networks for Sleep EEG Event Detection

arXiv: https://arxiv.org/abs/2005.07795 · HTML: https://mlsanity.com/paper/2005.07795/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 4 of 4)

- [Self-supervised Contrastive Learning for EEG-based Sleep Staging](https://mlsanity.com/paper/2109.07839/) (2021)
- [Advanced sleep spindle identification with neural networks](https://mlsanity.com/paper/2202.05158/) (2022)
- [The Portiloop: a deep learning-based open science tool for closed-loop brain stimulation](https://mlsanity.com/paper/2107.13473/) (2021)
- [From Sleep Staging to Spindle Detection: A Case Study on End-to-End Automated Sleep Analysis](https://mlsanity.com/paper/2505.05371/) (2025)

## Abstract (arXiv)

The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes. These events have been associated with biological processes and neurological disorders, making them a research topic in sleep medicine. However, manual detection limits their study because it is time-consuming and affected by significant inter-expert variability, motivating automatic approaches. We propose a deep learning approach based on convolutional and recurrent neural networks for sleep EEG event detection called Recurrent Event Detector (RED). RED uses one of two input representations: a) the time-domain EEG signal, or b) a complex spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT). Unlike previous approaches, a fixed time window is avoided and temporal context is integrated to better emulate the visual criteria of experts. When evaluated on the MASS dataset, our detectors outperform the state of the art in both sleep spindle and K-complex detection with a mean F1-score of at least 80.9% and 82.6%, respectively. Although the CWT-domain model obtained a similar performance than its time-domain counterpart, the former allows in principle a more interpretable input representation due to the use of a spectrogram. The proposed approach is event-agnostic and can be used directly to detect other types of sleep events.