AI RESEARCH
Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout
arXiv CS.LG
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ArXi:2409.01962v3 Announce Type: replace-cross 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.