Automatic detection of REM sleep in subjects without atonia

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Standard

Automatic detection of REM sleep in subjects without atonia. / Kempfner, Jacob; Jennum, Poul; Nikolic, Miki; Christensen, Gitte Julie; Sorensen, Helge B D.

I: I E E E Engineering in Medicine and Biology Society. Conference Proceedings, Bind 2012, 2012, s. 4242-5.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Kempfner, J, Jennum, P, Nikolic, M, Christensen, GJ & Sorensen, HBD 2012, 'Automatic detection of REM sleep in subjects without atonia', I E E E Engineering in Medicine and Biology Society. Conference Proceedings, bind 2012, s. 4242-5. https://doi.org/10.1109/EMBC.2012.6346903

APA

Kempfner, J., Jennum, P., Nikolic, M., Christensen, G. J., & Sorensen, H. B. D. (2012). Automatic detection of REM sleep in subjects without atonia. I E E E Engineering in Medicine and Biology Society. Conference Proceedings, 2012, 4242-5. https://doi.org/10.1109/EMBC.2012.6346903

Vancouver

Kempfner J, Jennum P, Nikolic M, Christensen GJ, Sorensen HBD. Automatic detection of REM sleep in subjects without atonia. I E E E Engineering in Medicine and Biology Society. Conference Proceedings. 2012;2012:4242-5. https://doi.org/10.1109/EMBC.2012.6346903

Author

Kempfner, Jacob ; Jennum, Poul ; Nikolic, Miki ; Christensen, Gitte Julie ; Sorensen, Helge B D. / Automatic detection of REM sleep in subjects without atonia. I: I E E E Engineering in Medicine and Biology Society. Conference Proceedings. 2012 ; Bind 2012. s. 4242-5.

Bibtex

@article{35e2fc1a17d54350b2a930f51d6eef53,
title = "Automatic detection of REM sleep in subjects without atonia",
abstract = "Idiopathic Rapid-Rye-Movement (REM) sleep Behavior Disorder (iRBD) is a strong early marker of Parkinson's Disease and is characterized by REM sleep without atonia (RSWA) and increased phasic muscle activity. Current proposed methods for detecting RSWA assume the presence of a manually scored hypnogram. In this study a full automatic REM sleep detector, using the EOG and EEG channels, is proposed. Based on statistical features, combined with subject specific feature scaling and post-processing of the classifier output, it was possible to obtain an mean accuracy of 0.96 with a mean sensititvity and specificity of 0.94 and 0.96 respectively.",
author = "Jacob Kempfner and Poul Jennum and Miki Nikolic and Christensen, {Gitte Julie} and Sorensen, {Helge B D}",
year = "2012",
doi = "10.1109/EMBC.2012.6346903",
language = "English",
volume = "2012",
pages = "4242--5",
journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
issn = "0589-1019",
publisher = "IEEE Signal Processing Society",

}

RIS

TY - JOUR

T1 - Automatic detection of REM sleep in subjects without atonia

AU - Kempfner, Jacob

AU - Jennum, Poul

AU - Nikolic, Miki

AU - Christensen, Gitte Julie

AU - Sorensen, Helge B D

PY - 2012

Y1 - 2012

N2 - Idiopathic Rapid-Rye-Movement (REM) sleep Behavior Disorder (iRBD) is a strong early marker of Parkinson's Disease and is characterized by REM sleep without atonia (RSWA) and increased phasic muscle activity. Current proposed methods for detecting RSWA assume the presence of a manually scored hypnogram. In this study a full automatic REM sleep detector, using the EOG and EEG channels, is proposed. Based on statistical features, combined with subject specific feature scaling and post-processing of the classifier output, it was possible to obtain an mean accuracy of 0.96 with a mean sensititvity and specificity of 0.94 and 0.96 respectively.

AB - Idiopathic Rapid-Rye-Movement (REM) sleep Behavior Disorder (iRBD) is a strong early marker of Parkinson's Disease and is characterized by REM sleep without atonia (RSWA) and increased phasic muscle activity. Current proposed methods for detecting RSWA assume the presence of a manually scored hypnogram. In this study a full automatic REM sleep detector, using the EOG and EEG channels, is proposed. Based on statistical features, combined with subject specific feature scaling and post-processing of the classifier output, it was possible to obtain an mean accuracy of 0.96 with a mean sensititvity and specificity of 0.94 and 0.96 respectively.

U2 - 10.1109/EMBC.2012.6346903

DO - 10.1109/EMBC.2012.6346903

M3 - Journal article

C2 - 23366864

VL - 2012

SP - 4242

EP - 4245

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

SN - 0589-1019

ER -

ID: 48474008