Clinical Neurology Unit, Udine University Hospital, 33100, Udine, Italy.
Department of Mathematics, Computer Science, and Physics, University of Udine, 33100, Udine, Italy.
Sci Data. 2022 Apr 19;9(1):177. doi: 10.1038/s41597-022-01272-y.
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.
多导睡眠图（PSG）是检测阻塞性睡眠呼吸暂停综合征（OSAS）的基本诊断方法。从历史上看，经过培训的医生一直根据 PSG 记录手动识别个体中的 OSAS 发作。对于中风患者来说，这种任务非常重要，因为在这种情况下，OSAS 与更高的死亡率和更严重的神经功能缺陷有关。不幸的是，每天发生的中风数量远远超过了多导睡眠图和专门的医疗保健专业人员的可用性。这项工作中的数据涉及到 30 名在意大利乌迪内大学医院中风病房住院的患者。与之前的研究不同，排除标准很少。因此，数据受到很大的噪声影响，并且个体可能患有多种合并症。每个患者实例都由来自多通道心电图、光体积描记术和多导睡眠图的夜间生命体征数据以及相关领域专家的 OSAS 注释组成。该数据集旨在支持开发基于常规监测生命体征的自动方法来检测 OSAS 事件，并能够在实际场景中工作。
