# SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging

arXiv: https://arxiv.org/abs/2105.13864 · HTML: https://mlsanity.com/paper/2105.13864/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 24 May 2021.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging](https://mlsanity.com/paper/1809.10932/) (2018)
- [U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging](https://mlsanity.com/paper/1910.11162/) (2019)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection](https://mlsanity.com/paper/2004.08515/) (2020)
- [A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series](https://mlsanity.com/paper/1707.03321/) (2017)
- [SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach](https://mlsanity.com/paper/1903.02108/) (2019)
- [Detecting causality in multivariate time series via non-uniform embedding](https://mlsanity.com/paper/1903.05842/) (2019)

## Built on by (most cited first, 30 of 32)

- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification](https://mlsanity.com/paper/2109.01824/) (2021)
- [CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals](https://mlsanity.com/paper/2106.13695/) (2021)
- [L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging](https://mlsanity.com/paper/2301.03441/) (2023)
- [Generalizable Sleep Staging via Multi-Level Domain Alignment](https://mlsanity.com/paper/2401.05363/) (2023)
- [HetEmotionNet: Two-Stream Heterogeneous Graph Recurrent Neural Network for Multi-modal Emotion Recognition](https://mlsanity.com/paper/2108.03354/) (2021)
- [SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography](https://mlsanity.com/paper/2202.05735/) (2022)
- [Interpretable and Robust AI in EEG Systems: A Survey](https://mlsanity.com/paper/2304.10755/) (2023)
- [A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications](https://mlsanity.com/paper/2206.08672/) (2022)
- [SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning](https://mlsanity.com/paper/2209.09452/) (2022)
- [S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models](https://mlsanity.com/paper/2310.06715/) (2023)
- [Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2412.12159/) (2024)
- [SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging](https://mlsanity.com/paper/2110.09966/) (2021)
- [PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition](https://mlsanity.com/paper/2209.13645/) (2022)
- [ST-USleepNet: A Spatial-Temporal Coupling Prominence Network for Multi-Channel Sleep Staging](https://mlsanity.com/paper/2408.11884/) (2024)
- [Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework](https://mlsanity.com/paper/2502.17481/) (2025)
- [SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learning](https://mlsanity.com/paper/2410.11200/) (2024)
- [SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages](https://mlsanity.com/paper/2307.05362/) (2023)
- [EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts](https://mlsanity.com/paper/2604.12579/) (2026)
- [Seeing your sleep stage: cross-modal distillation from EEG to infrared video](https://mlsanity.com/paper/2208.05814/) (2022)
- [Introducing Multimodal Paradigm for Learning Sleep Staging PSG via General-Purpose Model](https://mlsanity.com/paper/2509.22810/) (2025)
- [Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks](https://mlsanity.com/paper/2410.19819/) (2024)
- [sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging](https://mlsanity.com/paper/2501.16329/) (2025)
- [Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification](https://mlsanity.com/paper/2507.09826/) (2025)
- [Multi-Channel Differential Transformer for Cross-Domain Sleep Stage Classification with Heterogeneous EEG and EOG](https://mlsanity.com/paper/2508.15215/) (2025)
- [Combining Residual U-Net and Data Augmentation for Dense Temporal Segmentation of Spike Wave Discharges in Single-Channel EEG](https://mlsanity.com/paper/2601.00459/) (2026)
- [NeuroSleep: Neuromorphic Event-Driven Single-Channel EEG Sleep Staging for Edge-Efficient Sensing](https://mlsanity.com/paper/2602.15888/) (2026)
- [SleepVLM: A Rule-Grounded Vision-Language Model for Auditable Sleep Staging](https://mlsanity.com/paper/2603.26738/) (2026)
- [Complex Diffusion Maps with ω-Parameterized Kernels Revealing Inherent Harmonic Representations](https://mlsanity.com/paper/2605.01691/) (2026)
- [LGFNet: A CTC-Guided Local-Global Fusion Framework for Single-Channel Sleep Staging](https://mlsanity.com/paper/2607.25197/) (2026)

## Abstract (arXiv)

Sleep staging is fundamental for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively extract salient waves in multimodal sleep data; 2) How to capture the multi-scale transition rules among sleep stages; 3) How to adaptively seize the key role of specific modality for sleep staging. To address these challenges, we propose SalientSleepNet, a multimodal salient wave detection network for sleep staging. Specifically, SalientSleepNet is a temporal fully convolutional network based on the rm U2-Net architecture that is originally proposed for salient object detection in computer vision. It is mainly composed of two independent rm U2-like streams to extract the salient features from multimodal data, respectively. Meanwhile, the multi-scale extraction module is designed to capture multi-scale transition rules among sleep stages. Besides, the multimodal attention module is proposed to adaptively capture valuable information from multimodal data for the specific sleep stage. Experiments on the two datasets demonstrate that SalientSleepNet outperforms the state-of-the-art baselines. It is worth noting that this model has the least amount of parameters compared with the existing deep neural network models.