Literature DB >> 26589468

ISRUC-Sleep: A comprehensive public dataset for sleep researchers.

Sirvan Khalighi1, Teresa Sousa2, José Moutinho Santos3, Urbano Nunes2.   

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.
Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Automatic sleep stage classification; Effects of sleep disorder; Feature selection; Medication effects; Polysomnographic signals; Sleep dataset

Mesh:

Year:  2015        PMID: 26589468     DOI: 10.1016/j.cmpb.2015.10.013

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  11 in total

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4.  U-Sleep: resilient high-frequency sleep staging.

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Authors:  Diego Alvarez-Estevez; Roselyne M Rijsman
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Journal:  Biomed Eng Online       Date:  2022-09-12       Impact factor: 3.903

7.  MMASleepNet: A multimodal attention network based on electrophysiological signals for automatic sleep staging.

Authors:  Zheng Yubo; Luo Yingying; Zou Bing; Zhang Lin; Li Lei
Journal:  Front Neurosci       Date:  2022-08-16       Impact factor: 5.152

8.  Lying position classification based on ECG waveform and random forest during sleep in healthy people.

Authors:  Hongze Pan; Zhi Xu; Hong Yan; Yue Gao; Zhanghuang Chen; Jinzhong Song; Yu Zhang
Journal:  Biomed Eng Online       Date:  2018-08-30       Impact factor: 2.819

9.  Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes.

Authors:  Kangkyu Kwon; Shinjae Kwon; Woon-Hong Yeo
Journal:  Biosensors (Basel)       Date:  2022-03-02

10.  A Clinical Decision Support System for Sleep Staging Tasks With Explanations From Artificial Intelligence: User-Centered Design and Evaluation Study.

Authors:  Jeonghwan Hwang; Taeheon Lee; Honggu Lee; Seonjeong Byun
Journal:  J Med Internet Res       Date:  2022-01-19       Impact factor: 5.428

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