Validation of semiautomatic scoring of REM sleep without atonia in patients with RBD

D Guttowski; G Mayer; W H Oertel; K Kesper; T Rosenberg

doi:10.1016/j.sleep.2018.03.010

Validation of semiautomatic scoring of REM sleep without atonia in patients with RBD

Standard

Validation of semiautomatic scoring of REM sleep without atonia in patients with RBD. / Guttowski, D; Mayer, G; Oertel, W H; Kesper, K; Rosenberg, T.

in: SLEEP MED, Jahrgang 46, 06.2018, S. 107-113.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Guttowski, D, Mayer, G, Oertel, WH, Kesper, K & Rosenberg, T 2018, 'Validation of semiautomatic scoring of REM sleep without atonia in patients with RBD', SLEEP MED, Jg. 46, S. 107-113. https://doi.org/10.1016/j.sleep.2018.03.010

APA

Guttowski, D., Mayer, G., Oertel, W. H., Kesper, K., & Rosenberg, T. (2018). Validation of semiautomatic scoring of REM sleep without atonia in patients with RBD. SLEEP MED, 46, 107-113. https://doi.org/10.1016/j.sleep.2018.03.010

Vancouver

Guttowski D, Mayer G, Oertel WH, Kesper K, Rosenberg T. Validation of semiautomatic scoring of REM sleep without atonia in patients with RBD. SLEEP MED. 2018 Jun;46:107-113. https://doi.org/10.1016/j.sleep.2018.03.010

Bibtex

@article{61c7b42216f44bc09be54341e9fa2b43,

title = "Validation of semiautomatic scoring of REM sleep without atonia in patients with RBD",

abstract = "OBJECTIVE/BACKGROUND: To evaluate REM sleep without atonia (RSWA) in REM sleep behavior disorder (RBD) several automatic algorithms have been developed. We aimed to validate our algorithm (Mayer et al., 2008) in order to assess the following: (1). capability of the algorithm to differentiate between RBD, night terror (NT), somnambulism (SW), Restless legs syndrome (RLS), and obstructive sleep apnea (OSA), (2). the cut-off values for short (SMI) and long muscle activity (LMI), (3). which muscles qualify best for differential diagnosis, and (4). the comparability of RSWA and registered movements between automatic and visual analysis of videometry.PATIENTS/METHODS: RSWA was automatically scored according to Mayer et al., 2008 in polysomnographies of 20 RBD, 10 SW/NT, 10 RLS and 10 OSA patients. Receiver operating characteristic (ROC) curves were used to determine the sensitivity and specificity of SMI and LMI. Independent samples were calculated with t-tests. Boxplots were used for group comparison. The comparison between motor events by manual scoring and automatic analysis were performed with {"}Visual Basic for Applications{"} (VBA) for every hundredth second.RESULTS: Our method discriminates RBD from SW/NT, OSA and RLS with a sensitivity of 72.5% and a specificity of 86.7%. Automatic scoring identifies more movements than visual video scoring. Mentalis muscle discriminates the sleep disorders best, followed by FDS, which was only recorded in SW/NT. Cut-off values for RSWA are comparable to those found by other groups.CONCLUSION: The semi-automatic RSWA scoring method is capable to confirm RBD and to discriminate it with moderate sensitivity from other sleep disorders.",

keywords = "Aged, Algorithms, Case-Control Studies, Facial Muscles/physiology, Female, Humans, Male, Middle Aged, Night Terrors/physiopathology, Polysomnography/methods, REM Sleep Behavior Disorder/physiopathology, Restless Legs Syndrome/physiopathology, Sensitivity and Specificity, Sleep Apnea, Obstructive/physiopathology, Sleep, REM/physiology",

author = "D Guttowski and G Mayer and Oertel, {W H} and K Kesper and T Rosenberg",

year = "2018",

month = jun,

doi = "10.1016/j.sleep.2018.03.010",

language = "English",

volume = "46",

pages = "107--113",

journal = "SLEEP MED",

issn = "1389-9457",

publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Validation of semiautomatic scoring of REM sleep without atonia in patients with RBD

AU - Guttowski, D

AU - Mayer, G

AU - Oertel, W H

AU - Kesper, K

AU - Rosenberg, T

PY - 2018/6

Y1 - 2018/6

N2 - OBJECTIVE/BACKGROUND: To evaluate REM sleep without atonia (RSWA) in REM sleep behavior disorder (RBD) several automatic algorithms have been developed. We aimed to validate our algorithm (Mayer et al., 2008) in order to assess the following: (1). capability of the algorithm to differentiate between RBD, night terror (NT), somnambulism (SW), Restless legs syndrome (RLS), and obstructive sleep apnea (OSA), (2). the cut-off values for short (SMI) and long muscle activity (LMI), (3). which muscles qualify best for differential diagnosis, and (4). the comparability of RSWA and registered movements between automatic and visual analysis of videometry.PATIENTS/METHODS: RSWA was automatically scored according to Mayer et al., 2008 in polysomnographies of 20 RBD, 10 SW/NT, 10 RLS and 10 OSA patients. Receiver operating characteristic (ROC) curves were used to determine the sensitivity and specificity of SMI and LMI. Independent samples were calculated with t-tests. Boxplots were used for group comparison. The comparison between motor events by manual scoring and automatic analysis were performed with "Visual Basic for Applications" (VBA) for every hundredth second.RESULTS: Our method discriminates RBD from SW/NT, OSA and RLS with a sensitivity of 72.5% and a specificity of 86.7%. Automatic scoring identifies more movements than visual video scoring. Mentalis muscle discriminates the sleep disorders best, followed by FDS, which was only recorded in SW/NT. Cut-off values for RSWA are comparable to those found by other groups.CONCLUSION: The semi-automatic RSWA scoring method is capable to confirm RBD and to discriminate it with moderate sensitivity from other sleep disorders.

AB - OBJECTIVE/BACKGROUND: To evaluate REM sleep without atonia (RSWA) in REM sleep behavior disorder (RBD) several automatic algorithms have been developed. We aimed to validate our algorithm (Mayer et al., 2008) in order to assess the following: (1). capability of the algorithm to differentiate between RBD, night terror (NT), somnambulism (SW), Restless legs syndrome (RLS), and obstructive sleep apnea (OSA), (2). the cut-off values for short (SMI) and long muscle activity (LMI), (3). which muscles qualify best for differential diagnosis, and (4). the comparability of RSWA and registered movements between automatic and visual analysis of videometry.PATIENTS/METHODS: RSWA was automatically scored according to Mayer et al., 2008 in polysomnographies of 20 RBD, 10 SW/NT, 10 RLS and 10 OSA patients. Receiver operating characteristic (ROC) curves were used to determine the sensitivity and specificity of SMI and LMI. Independent samples were calculated with t-tests. Boxplots were used for group comparison. The comparison between motor events by manual scoring and automatic analysis were performed with "Visual Basic for Applications" (VBA) for every hundredth second.RESULTS: Our method discriminates RBD from SW/NT, OSA and RLS with a sensitivity of 72.5% and a specificity of 86.7%. Automatic scoring identifies more movements than visual video scoring. Mentalis muscle discriminates the sleep disorders best, followed by FDS, which was only recorded in SW/NT. Cut-off values for RSWA are comparable to those found by other groups.CONCLUSION: The semi-automatic RSWA scoring method is capable to confirm RBD and to discriminate it with moderate sensitivity from other sleep disorders.

KW - Aged

KW - Algorithms

KW - Case-Control Studies

KW - Facial Muscles/physiology

KW - Female

KW - Humans

KW - Male

KW - Middle Aged

KW - Night Terrors/physiopathology

KW - Polysomnography/methods

KW - REM Sleep Behavior Disorder/physiopathology

KW - Restless Legs Syndrome/physiopathology

KW - Sensitivity and Specificity

KW - Sleep Apnea, Obstructive/physiopathology

KW - Sleep, REM/physiology

U2 - 10.1016/j.sleep.2018.03.010

DO - 10.1016/j.sleep.2018.03.010

M3 - SCORING: Journal article

C2 - 29773203

VL - 46

SP - 107

EP - 113

JO - SLEEP MED

JF - SLEEP MED

SN - 1389-9457

ER -