ArticleScientific data2025
A multimodal dataset for training deep learning models aimed at detecting and analyzing sleep apnea.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Multicenter Validation of an AI-Based Snoring Model vs. Wearables for Diagnosing Sleep Apnea.Nature and science of sleep · 2026Article
- NRGAMTE: Neurophysiological Residual Gated Attention Multimodal Transformer Encoder for Sleep Disorder Detection.Brain sciences · 2025Article
- A multimodal dataset for training deep learning models aimed at detecting and analyzing sleep apnea.Scientific data · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep Apnea Syndrome (SAS) is a serious respiratory disorder that can lead to a range of complications, including hypertension, arrhythmias, cognitive impair- ment, and metabolic disturbances. Due to the insidious nature of its symptoms, patients often fail to recognize the condition, and clinical screening is both time- consuming and resource-intensive. To address these challenges, we have developed a comprehensive dataset that integrates data from Polysomnography (PSG) devices with synchronized audio recordings. This dataset has been rigorously annotated by expert medical professionals based on PSG monitoring data, ensur- ing its accuracy and reliability. Our objective is to provide a publicly available, standardized, and high-quality data resource for the development and applica- tion of deep learning models in the field of sleep apnea syndrome. This dataset is designed to enhance diagnostic accuracy and efficiency while promoting advanced scientific research and technological innovation in this domain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.