title: ISRUC-Sleep: A comprehensive public dataset for sleep researchers.
abstract: 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.
authors.affiliation: Institute of Systems and Robotics (ISR-UC), Department of Electrical and Computer Engineering, University of Coimbra, Portugal. Electronic address: skhalighi@isr.uc.pt.
authors.forename: Sirvan
authors.initials: S
authors.lastname: Khalighi
authors.affiliation: Institute of Systems and Robotics (ISR-UC), Department of Electrical and Computer Engineering, University of Coimbra, Portugal.
authors.forename: Teresa
authors.initials: T
authors.lastname: Sousa
authors.affiliation: Sleep Medicine Centre, The Central Hospital of University of Coimbra (CHUC), Portugal.
authors.forename: José Moutinho
authors.initials: JM
authors.lastname: Santos
authors.affiliation: Institute of Systems and Robotics (ISR-UC), Department of Electrical and Computer Engineering, University of Coimbra, Portugal.
authors.forename: Urbano
authors.initials: U
authors.lastname: Nunes
mesh_terms: D035843:Biomedical Research / Q000379:methods; D003628:Database Management Systems; D016208:Databases, Factual; D057286:Electronic Health Records / Q000458:organization & administration*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D009323:Natural Language Processing; D017286:Polysomnography / Q000706:statistics & numerical data*; D011174:Portugal / Q000453:epidemiology; D012893:Sleep Wake Disorders / Q000175:diagnosis* / Q000453:epidemiology / Q000628:therapy*; D018875:Vocabulary, Controlled
keywords: Automatic sleep stage classification; Effects of sleep disorder; Feature selection; Medication effects; Polysomnographic signals; Sleep dataset