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    Monitoring of patients suffering from REM sleep behavior disorder

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    Authors
    Yang, Xiaodong
    Shah, Syed Aziz
    Ren, Aifeng
    Zhao, Nan
    Zhao, Jianxun
    Hu, Fangming
    Zhang, Zhiya
    Zhao, Wei
    Ur-Rehman, Masood
    Alomainy, Akram
    Affiliation
    Xidian University
    University of Bedfordshire
    Queen Mary University of London
    Issue Date
    2018-04-16
    Subjects
    Rapid eye movement (REM)
    wireless sensing
    REM behavior sleep disorder (RBD)
    
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    Abstract
    Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia that involves involuntary, unwanted, and random movements of a dreaming patient. Typically, these dreams contain violent activities. There is a high likelihood of the patient being injured or hurting his bed-partner as a result of these enactments. Continuous monitoring of sleeping RBD patients can prevent these harmful events through timely intervention. This paper presents a novel method for continuous observation of RBD patients exploiting fine-grained amplitude and phase information of the wireless channel response. The variations in the wireless channel response as a result of different patient movements are assessed and used to identify RBD episodes. The data obtained are classified using a support vector machine and deliver an accuracy level of more than 90%. To the best of authors' knowledge, this is a first attempt at using radio frequency signals to sense RBD in real time.
    Citation
    Yang X, Shah S, Ren A, Zhao N, Zhao J, Hu F, Zhang Z, Zhao W, Rehman M, Alomainy A (2018) 'Monitoring of patients suffering from REM sleep behavior disorder', IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2 (2), pp.138-143.
    Publisher
    Institute of Electrical and Electronics Engineers Inc.
    Journal
    IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology
    URI
    http://hdl.handle.net/10547/624324
    DOI
    10.1109/JERM.2018.2827705
    Additional Links
    https://ieeexplore.ieee.org/document/8338401/
    Type
    Article
    Language
    en
    ISSN
    2469-7249
    EISSN
    2469-7249
    ae974a485f413a2113503eed53cd6c53
    10.1109/JERM.2018.2827705
    Scopus Count
    Collections
    Computing

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