ArticleSensors (Basel, Switzerland)2023
Sleep CLIP: A Multimodal Sleep Staging Model Based on Sleep Signals and Sleep Staging Labels.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Assessing the Diagnostic Accuracy of BiomedCLIP for Detecting Contrast Use and Esophageal Strictures in Pediatric Radiography.Journal of clinical medicine · 2026Article
- AI digital-twin ecosystem translating gut-microbiome-neuroimmune signals into precision sleep-mood interventions.Frontiers in psychiatry · 2026Review
- Comparative Evaluation of Large Language and Multimodal Models in Detecting Spinal Stabilization Systems on X-Ray Images.Journal of clinical medicine · 2025Article
- Time-Series Representation Feature Refinement with a Learnable Masking Augmentation Framework in Contrastive Learning.Sensors (Basel, Switzerland) · 2024Article
- Machine learning-empowered sleep staging classification using multi-modality signals.BMC medical informatics and decision making · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging tasks with high accuracy has always remained the main goal of sleep staging algorithm designers. This study is aimed at designing a multimodal model based on the CLIP model that is more suitable for sleep staging tasks using sleep signals and labels. The pre-training efforts of the model involve five different training sets. Finally, the proposed method is tested on two training sets (EDF-39 and EDF-153), with accuracies of 87.3 and 85.4%, respectively.
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Registered trials
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