Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings

Research output: Contribution to journalJournal articleResearchpeer-review

Standard

Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings. / Kempfner, Jacob; Sorensen, Helge B D; Nikolic, Miki; Jennum, Poul.

In: Journal of Clinical Neurophysiology, Vol. 31, No. 5, 2014, p. 409-415.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Kempfner, J, Sorensen, HBD, Nikolic, M & Jennum, P 2014, 'Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings', Journal of Clinical Neurophysiology, vol. 31, no. 5, pp. 409-415. https://doi.org/10.1097/WNP.0000000000000065

APA

Kempfner, J., Sorensen, H. B. D., Nikolic, M., & Jennum, P. (2014). Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings. Journal of Clinical Neurophysiology, 31(5), 409-415. https://doi.org/10.1097/WNP.0000000000000065

Vancouver

Kempfner J, Sorensen HBD, Nikolic M, Jennum P. Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings. Journal of Clinical Neurophysiology. 2014;31(5):409-415. https://doi.org/10.1097/WNP.0000000000000065

Author

Kempfner, Jacob ; Sorensen, Helge B D ; Nikolic, Miki ; Jennum, Poul. / Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings. In: Journal of Clinical Neurophysiology. 2014 ; Vol. 31, No. 5. pp. 409-415.

Bibtex

@article{b391f369dff441f9a507aab942f77164,
title = "Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings",
abstract = "SUMMARY: Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is most likely the earliest sign of Parkinson's Disease (PD) and is characterized by REM sleep without atonia (RSWA) and consequently increased muscle activity. However, some muscle twitching in normal subjects occurs during REM sleep.PURPOSE: There are no generally accepted methods for evaluation of this activity and a normal range has not been established. Consequently, there is a need for objective criteria.METHOD: In this study we propose a full-automatic method for detection of RSWA. REM sleep identification was based on the electroencephalography and electrooculography channels, while the abnormal high muscle activity was detected from the electromyography channels, in this case the submentalis combined with left and right anterior tibialis. RSWA was identified by considering it an outlier problem, in which the number of outliers during REM sleep was used as a quantitative measure of muscle activity.RESULTS: The proposed method was able to automatically separate all iRBD test subjects from healthy elderly controls and subjects with periodic limb movement disorder.CONCLUSION: The proposed work is considered a potential automatic method for early detection of PD.",
author = "Jacob Kempfner and Sorensen, {Helge B D} and Miki Nikolic and Poul Jennum",
year = "2014",
doi = "10.1097/WNP.0000000000000065",
language = "English",
volume = "31",
pages = "409--415",
journal = "Journal of Clinical Neurophysiology",
issn = "0736-0258",
publisher = "Lippincott Williams & Wilkins",
number = "5",

}

RIS

TY - JOUR

T1 - Early Automatic Detection of Parkinson's Disease Based on Sleep Recordings

AU - Kempfner, Jacob

AU - Sorensen, Helge B D

AU - Nikolic, Miki

AU - Jennum, Poul

PY - 2014

Y1 - 2014

N2 - SUMMARY: Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is most likely the earliest sign of Parkinson's Disease (PD) and is characterized by REM sleep without atonia (RSWA) and consequently increased muscle activity. However, some muscle twitching in normal subjects occurs during REM sleep.PURPOSE: There are no generally accepted methods for evaluation of this activity and a normal range has not been established. Consequently, there is a need for objective criteria.METHOD: In this study we propose a full-automatic method for detection of RSWA. REM sleep identification was based on the electroencephalography and electrooculography channels, while the abnormal high muscle activity was detected from the electromyography channels, in this case the submentalis combined with left and right anterior tibialis. RSWA was identified by considering it an outlier problem, in which the number of outliers during REM sleep was used as a quantitative measure of muscle activity.RESULTS: The proposed method was able to automatically separate all iRBD test subjects from healthy elderly controls and subjects with periodic limb movement disorder.CONCLUSION: The proposed work is considered a potential automatic method for early detection of PD.

AB - SUMMARY: Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is most likely the earliest sign of Parkinson's Disease (PD) and is characterized by REM sleep without atonia (RSWA) and consequently increased muscle activity. However, some muscle twitching in normal subjects occurs during REM sleep.PURPOSE: There are no generally accepted methods for evaluation of this activity and a normal range has not been established. Consequently, there is a need for objective criteria.METHOD: In this study we propose a full-automatic method for detection of RSWA. REM sleep identification was based on the electroencephalography and electrooculography channels, while the abnormal high muscle activity was detected from the electromyography channels, in this case the submentalis combined with left and right anterior tibialis. RSWA was identified by considering it an outlier problem, in which the number of outliers during REM sleep was used as a quantitative measure of muscle activity.RESULTS: The proposed method was able to automatically separate all iRBD test subjects from healthy elderly controls and subjects with periodic limb movement disorder.CONCLUSION: The proposed work is considered a potential automatic method for early detection of PD.

U2 - 10.1097/WNP.0000000000000065

DO - 10.1097/WNP.0000000000000065

M3 - Journal article

C2 - 25271677

VL - 31

SP - 409

EP - 415

JO - Journal of Clinical Neurophysiology

JF - Journal of Clinical Neurophysiology

SN - 0736-0258

IS - 5

ER -

ID: 135274182