文章基本信息

标题：OSASUD: A dataset of stroke unit recordings for the detection of Obstructive Sleep Apnea Syndrome
本地全文：下载
作者：andrea Bernardini ; Andrea Brunello ; Gian Luigi Gigli 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-10
DOI：10.1038/s41597-022-01272-y
语种：English
出版社：Nature Publishing Group
摘要：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 defcits . 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 afected by noise, and individuals may sufer 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 .