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

Abstract

SUMMARYRapid 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/intraobserver 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.

Increased Motor Activity During REM Sleep Is Linked with Dopamine Function in Idiopathic REM Sleep Behavior Disorder and Parkinson Disease

“…Our group has recently published a study with the aim of finding the most sensitive baseline for measuring EMG activity during REM sleep. 47 However, such a baseline should also be developed when analyzing the whole sleep period, taking into account possible changes in muscular tone across the sleep stages.…”

Detection of REM sleep behaviour disorder by automated polysomnography analysis

“…This was then used to derive features that depict the number of motor activity events, quantified by duration and percentage of REM epochs, to distinguish RBD from other individuals. Despite the abundance of studies on RBD detection, these objective metrics have been applied to relatively small RBD cohorts to date (ranging from 10 to 31 patients), achieving variable sensitivity and specificity (0.74-1.00 and 0.71-1.00, respectively) (Burns et al 2007;Ferri et al 2008Ferri et al , 2010Kempfner et al 2013b;Frauscher et al 2014;Kempfner et al 2014;Frandsen et al 2015). In a preliminary study we showed that using an ensemble of established techniques improved RBD detection, and can be further enhanced with the incorporation of sleep architectural features (Cooray et al 2018).…”

External validation of a data‐driven algorithm for muscular activity identification during sleep

“…For RBD detection, several automated algorithms have been proposed (Ferri et al., , ; Frandsen et al., ; Frauscher et al., ; Kempfner & Nikolic, ; Mayer et al., ) and compared (Cesari et al., ). The algorithm described in Frauscher et al.…”

“…For RBD detection, several automated algorithms have been proposed (Ferri et al, 2008(Ferri et al, , 2010Frandsen et al, 2015;Frauscher et al, 2014;Kempfner & Nikolic, 2014;Mayer et al, 2008) and compared . The algorithm described in Frauscher et al (2014) has been recently applied to a big dataset (Haba-rubio et al, 2018), but a formal validation on this dataset is lacking.…”

“…Furthermore, the use of objective automated methods may help in identifying possible reasons for sleep disturbances and fragmentation. Several groups have already developed automated methods for identifying PLMS (Alvarez-Estevez, 2016;Ferri et al, 2005;Huang et al, 2015;Moore et al, 2014;Stefani, Heidbreder, Hackner, & Högl, 2017;Wetter et al, 2004), and for detecting RSWA and RBD (Ferri et al, 2008(Ferri et al, , 2010Frandsen, Nikolic, Zoetmulder, Kempfner, & Jennum, 2015;Frauscher et al, 2014;Kempfner & Nikolic, 2014;Mayer et al, 2008). Moreover, our group has recently developed a data-driven algorithm based on machine learning techniques that is able to distinguish iRBD patients from patients suffering from PLM disorder (PLMD) and healthy controls (HCs) with higher classification performances than other available automated methods (Cesari et al, 2019).…”