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

Research output: Contribution to journalJournal articleResearchpeer-review

  • Rune Frandsen
  • Miki Nikolic
  • Marielle Zoetmulder
  • Lykke Kempfner
  • Jennum, Poul

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.

Original languageEnglish
JournalJournal of Sleep Research Online
Volume24
Issue number5
Pages (from-to)583-90
Number of pages8
ISSN1365-2869
DOIs
Publication statusPublished - Oct 2015

    Research areas

  • Adult, Aged, Algorithms, Automation, Dreams, Electromyography, Female, Humans, Male, Middle Aged, Motor Activity, Neck Muscles, Parkinson Disease, Polysomnography, Psychomotor Agitation, REM Sleep Behavior Disorder, Sleep, REM

ID: 162711699