# Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout

arXiv: https://arxiv.org/abs/2409.01962 · HTML: https://mlsanity.com/paper/2409.01962/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Learning Depth-Guided Convolutions for Monocular 3D Object Detection](https://mlsanity.com/paper/1912.04799/) (2019)
- [Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification](https://mlsanity.com/paper/1805.06546/) (2018)
- [Inter-database validation of a deep learning approach for automatic sleep scoring](https://mlsanity.com/paper/2009.10365/) (2020)
- [A Large Collection of Real-world Pediatric Sleep Studies](https://mlsanity.com/paper/2102.13284/) (2021)
- [D^2Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos](https://mlsanity.com/paper/2111.07774/) (2021)
- [ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training](https://mlsanity.com/paper/2107.04470/) (2021)
- [Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG](https://mlsanity.com/paper/2207.06921/) (2022)
- [A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging](https://mlsanity.com/paper/2112.07252/) (2021)

## Abstract (arXiv)

Sleep stages play an important role in identifying sleep patterns and diagnosing sleep disorders. In this study, we present an automated sleep stage classifier called the Attentive Dilated Convolutional Neural Network (AttDiCNN), which uses deep learning methodologies to address challenges related to data heterogeneity, computational complexity, and reliable and automatic sleep staging. We employed a force-directed layout based on the visibility graph to capture the most significant information from the EEG signals, thereby representing the spatial-temporal features. The proposed network consists of three modules: the Localized Spatial Feature Extraction Network (LSFE), Spatio-Temporal-Temporal Long Retention Network (S2TLR), and Global Averaging Attention Network (G2A). The LSFE captures spatial information from sleep data, the S2TLR is designed to extract the most pertinent information in long-term contexts, and the G2A reduces computational overhead by aggregating information from the LSFE and S2TLR. We evaluated the performance of our model on three comprehensive and publicly accessible datasets, achieving state-of-the-art accuracies of 98.56%, 99.66%, and 99.08% for the EDFX, HMC, and NCH datasets, respectively, while maintaining a low computational complexity with 1.4 M parameters. Our proposed architecture surpasses existing methodologies in several performance metrics, thereby proving its potential as an automated tool for clinical settings.