[2409.01962] Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout

Electrical Engineering and Systems Science > Signal Processing arXiv:2409.01962 (eess) [Submitted on 21 Aug 2024 (v1), last revised 7 Apr 2026 (this version, v3)] Title:Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout Authors:Md Jobayer, Md Mehedi Hasan Shawon, Tasfin Mahmud, Md. Borhan Uddin Antor, Arshad M. Chowdhury View a PDF of the paper titled Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout, by Md Jobayer and 4 other authors View PDF HTML (experimental) Abstract: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 compu...