ArticleScientific data2022
OSASUD: A dataset of stroke unit recordings for the detection of Obstructive Sleep Apnea Syndrome.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning.Npj biological timing and sleep · 2026Article
- Redefining obstructive sleep apnea diagnosis: An attention augmented CNN-BiLSTM hybrid alternative to traditional PSG testing.MethodsX · 2026Article
- Polysomnography Dataset for Sleep Analysis in Ischemic Stroke Patients.Scientific data · 2026Article
- Transformer-based deep learning approach for obstructive sleep apnea detection using single-lead ECG.Frontiers in artificial intelligence · 2026Article
- A multimodal dataset for training deep learning models aimed at detecting and analyzing sleep apnea.Scientific data · 2025Article
- Detection and Severity Classification of Sleep Apnea Using Continuous Wearable SpOSensors (Basel, Switzerland) · 2025Article
- AI-driven sleep apnea screening with overnight blood oxygen saturation: current practices and future directions.Frontiers in digital health · 2025Review
- Developing probabilistic ensemble machine learning models for home-based sleep apnea screening using overnight SpO2 data at varying data granularity.Sleep & breathing = Schlaf & Atmung · 2024Article
- An implantable system for opioid safety.Device · 2024Article
- Article
- Adoption of Transformer Neural Network to Improve the Diagnostic Performance of Oximetry for Obstructive Sleep Apnea.Sensors (Basel, Switzerland) · 2023Article
- Sleep and Stroke: Opening Our Eyes to Current Knowledge of a Key Relationship.Current neurology and neuroscience reports · 2022Review
- OSASUD: A dataset of stroke unit recordings for the detection of Obstructive Sleep Apnea Syndrome.Scientific data · 2022Article
- Article
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5 authors.
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Abstract
Polysomnography (PSG) is a fundamental diagnostical method for the detection of Obstructive Sleep Apnea Syndrome (OSAS). Historically, trained physicians have been manually identifying OSAS episodes in individuals based on PSG recordings. Such a task is highly important for stroke patients, since in such cases OSAS is linked to higher mortality and worse neurological deficits. Unfortunately, the number of strokes per day vastly outnumbers the availability of polysomnographs and dedicated healthcare professionals. The data in this work pertains to 30 patients that were admitted to the stroke unit of the Udine University Hospital, Italy. Unlike previous studies, exclusion criteria are minimal. As a result, data are strongly affected by noise, and individuals may suffer from several comorbidities. Each patient instance is composed of overnight vital signs data deriving from multi-channel ECG, photoplethysmography and polysomnography, and related domain expert's OSAS annotations. The dataset aims to support the development of automated methods for the detection of OSAS events based on just routinely monitored vital signs, and capable of working in a real-world scenario.
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