Sleep-stage efficient classification using a lightweight self-supervised model
Quick Take
This study presents a simplified mulEEG model using 1D-convolutions and a Linear SVM classifier for enhanced sleep stage classification. The approach achieved higher performance with concatenated features and reduced data volume, promising improvements in clinical sleep assessments.
Key Points
- The mulEEG model was simplified by using ResNet-18 with 1D-convolutions.
- Concatenated features outperformed linear evaluations of the original mulEEG model.
- Reducing data volume improved the cost-benefit ratio for training.
- The approach can enhance clinical assessments of sleep disorders.
- This model can be extended to other biological signal classification tasks.
Article Content
From source RSS / original summaryarXiv:2605. 26295v1 Announce Type: new Abstract: 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.
\textbf{Methods:} The mulEEG model, which learns electroencephalogram 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.
\textbf{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. \textbf{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.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CV
See more →Evi-Steer: Learning to Steer Biomedical Vision-Language Models through Efficient and Generalizable Evidential Tuning
Evi-Steer introduces a novel evidential tuning framework for BiomedCLIP, achieving 0.11% parameter updates while enhancing uncertainty-aware fine-tuning. It outperforms state-of-the-art methods across 15 biomedical imaging datasets, proving effective in few-shot learning and domain shifts for clinical applications.