Analysis of automated quantification of motor activity in REM sleep behaviour disorder

TY - JOUR

T1 - Analysis of automated quantification of motor activity in REM sleep behaviour disorder

AU - Frandsen, Rune

AU - Nikolic, Miki

AU - Zoetmulder, Marielle

AU - Kempfner, Lykke

AU - Jennum, Poul

N1 - © 2015 European Sleep Research Society.

PY - 2015/10/1

Y1 - 2015/10/1

N2 - Rapid eye movement (REM) sleep behaviour disorder (RBD) is characterized by dream enactment and REM sleep without atonia. Atonia is evaluated on the basis of visual criteria, but there is a need for more objective, quantitative measurements. We aimed to define and optimize a method for establishing baseline and all other parameters in automatic quantifying submental motor activity during REM sleep. We analysed the electromyographic activity of the submental muscle in polysomnographs of 29 patients with idiopathic RBD (iRBD), 29 controls and 43 Parkinson's (PD) patients. Six adjustable parameters for motor activity were defined. Motor activity was detected and quantified automatically. The optimal parameters for separating RBD patients from controls were investigated by identifying the greatest area under the receiver operating curve from a total of 648 possible combinations. The optimal parameters were validated on PD patients. Automatic baseline estimation improved characterization of atonia during REM sleep, as it eliminates inter/intra-observer variability and can be standardized across diagnostic centres. We found an optimized method for quantifying motor activity during REM sleep. The method was stable and can be used to differentiate RBD from controls and to quantify motor activity during REM sleep in patients with neurodegeneration. No control had more than 30% of REM sleep with increased motor activity; patients with known RBD had as low activity as 4.5%. We developed and applied a sensitive, quantitative, automatic algorithm to evaluate loss of atonia in RBD patients.

AB - Rapid eye movement (REM) sleep behaviour disorder (RBD) is characterized by dream enactment and REM sleep without atonia. Atonia is evaluated on the basis of visual criteria, but there is a need for more objective, quantitative measurements. We aimed to define and optimize a method for establishing baseline and all other parameters in automatic quantifying submental motor activity during REM sleep. We analysed the electromyographic activity of the submental muscle in polysomnographs of 29 patients with idiopathic RBD (iRBD), 29 controls and 43 Parkinson's (PD) patients. Six adjustable parameters for motor activity were defined. Motor activity was detected and quantified automatically. The optimal parameters for separating RBD patients from controls were investigated by identifying the greatest area under the receiver operating curve from a total of 648 possible combinations. The optimal parameters were validated on PD patients. Automatic baseline estimation improved characterization of atonia during REM sleep, as it eliminates inter/intra-observer variability and can be standardized across diagnostic centres. We found an optimized method for quantifying motor activity during REM sleep. The method was stable and can be used to differentiate RBD from controls and to quantify motor activity during REM sleep in patients with neurodegeneration. No control had more than 30% of REM sleep with increased motor activity; patients with known RBD had as low activity as 4.5%. We developed and applied a sensitive, quantitative, automatic algorithm to evaluate loss of atonia in RBD patients.

KW - Adult

KW - Aged

KW - Algorithms

KW - Automation

KW - Dreams

KW - Electromyography

KW - Female

KW - Humans

KW - Male

KW - Middle Aged

KW - Motor Activity

KW - Neck Muscles

KW - Parkinson Disease

KW - Polysomnography

KW - Psychomotor Agitation

KW - REM Sleep Behavior Disorder

KW - Sleep, REM

U2 - 10.1111/jsr.12304

DO - 10.1111/jsr.12304

M3 - Journal article

C2 - 25923472

SN - 1365-2869

VL - 24

SP - 583

EP - 590

JO - Journal of Sleep Research

JF - Journal of Sleep Research

IS - 5

ER -