[2501.02949] MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification

Computer Science > Machine Learning arXiv:2501.02949 (cs) [Submitted on 6 Jan 2025 (v1), last revised 24 Mar 2026 (this version, v2)] Title:MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification Authors:Stephan Goerttler, Yucheng Wang, Emadeldeen Eldele, Min Wu, Fei He View a PDF of the paper titled MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification, by Stephan Goerttler and 4 other authors View PDF Abstract:Recent advancements in machine learning-based signal analysis, coupled with open data initiatives, have fuelled efforts in automatic sleep stage classification. Despite the proliferation of classification models, few have prioritised reducing model complexity, which is a crucial factor for practical applications. In this work, we introduce Multi-Scale and Attention Convolutional Neural Network (MSA-CNN), a lightweight architecture featuring as few as ~10,000 parameters. MSA-CNN leverages a novel multi-scale module employing complementary pooling to eliminate redundant filter parameters and dense convolutions. Model complexity is further reduced by separating temporal and spatial feature extraction and using cost-effective global spatial convolutions. This separation of tasks not only reduces model complexity but also mirrors the approach used by human experts in sleep stage scoring. We evaluated both small and large configurations of MSA-CNN against nine state-of-the-art baseline models across three public...