期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 124, 期 -, 页码 180-192出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2015.10.013
关键词
Sleep dataset; Automatic sleep stage classification; Polysomnographic signals; Effects of sleep disorder; Medication effects; Feature selection
类别
资金
- Portuguese Foundation for Science and Technology (FCT) [SFRH/BD/81828/2011, SFRH/BD/80735/2011]
- QREN
- FEDER [CENTRO-01-0202-FEDER-011530]
- Fundação para a Ciência e a Tecnologia [SFRH/BD/81828/2011, SFRH/BD/80735/2011] Funding Source: FCT
To facilitate the performance comparison of new methods for sleep patterns analysis, datasets with quality content, publicly-available, are very important and useful. We introduce an open-access comprehensive sleep dataset, called ISRUC-Sleep. The data were obtained from human adults, including healthy subjects, subjects with sleep disorders, and subjects under the effect of sleep medication. Each recording was randomly selected between PSG recordings that were acquired by the Sleep Medicine Centre of the Hospital of Coimbra University (CHUC). The dataset comprises three groups of data: (1) data concerning 100 subjects, with one recording session per subject; (2) data gathered from 8 subjects; two recording sessions were performed per subject, and (3) data collected from one recording session related to 10 healthy subjects. The polysomnography (PSG) recordings, associated with each subject, were visually scored by two human experts. Comparing the existing sleep-related public datasets, ISRUC-Sleep provides data of a reasonable number of subjects with different characteristics such as: data useful for studies involving changes in the PSG signals over time; and data of healthy subjects useful for studies involving comparison of healthy subjects with the patients, suffering from sleep disorders. This dataset was created aiming to complement existing datasets by providing easy-to apply data collection with some characteristics not covered yet. ISRUC-Sleep can be useful for analysis of new contributions: (i) in biomedical signal processing; (ii) in development of ASSC methods; and (iii) on sleep physiology studies. To evaluate and compare new contributions, which use this dataset as a benchmark, results of applying a subject-independent automatic sleep stage classification (ASSC) method on ISRUC-Sleep dataset are presented. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
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