Sleep-Stage Efficient Classification Using a Lightweight Self-Supervised Model

Eldiane Borges dos Santos Durães, João Batista Florindo

2025

Abstract

Background and Objective: Accurate classification of sleep stages is crucial for diagnosing sleep disorders and automating this process can significantly enhance clinical assessments. This study aims to explore the use of a self-supervised model (more specifically, an adapted version of mulEEG) combined with a Linear SVM classifier to improve sleep stage classification. Methods: The mulEEG model, which learns electroen-cephalogram signal representations in a self-supervised manner, was simplified here by replacing ResNet-50 with 1D-convolutions used as time series encoder by a ResNet-18 backbone. Two other adaptations were conducted: the first one evaluated different configurations of the model and data volume for training, while the second tested the effectiveness of time series features, spectrogram features, and their concatenation as inputs to a Linear SVM classifier. Results: The results showed that reducing the volume of data offered a better cost-benefit ratio compared to simplifying the model. Using the concatenated features with ResNet-18 also outperformed the linear evaluations of the original mulEEG model, achieving higher classification performance. Conclusions: Simplifying the mulEEG model to extract features and pairing it with a robust classifier leads to more efficient and accurate sleep stage classification. This approach holds promise for improving clinical sleep assessments and can be extended to other biological signal classification tasks.

Paper Citation

in Harvard Style

Durães E. and Florindo J. (2025). Sleep-Stage Efficient Classification Using a Lightweight Self-Supervised Model. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 972-979. DOI: 10.5220/0013367900003912

in Bibtex Style

@conference{visapp25,
author={Eldiane Durães and João Florindo},
title={Sleep-Stage Efficient Classification Using a Lightweight Self-Supervised Model},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2025},
pages={972-979},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013367900003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Sleep-Stage Efficient Classification Using a Lightweight Self-Supervised Model
SN - 978-989-758-728-3
AU - Durães E.
AU - Florindo J.
PY - 2025
SP - 972
EP - 979
DO - 10.5220/0013367900003912
PB - SciTePress