Comparison of computerized methods for rapid eye movement sleep without atonia detection

Abstract

Rapid eye movement (REM) sleep without atonia detection is a prerequisite for diagnosis of REM sleep behavior disorder (RBD). As the visual gold standard method is time-consuming and subjective, several automated methods have been proposed. This study aims to compare their performances: The REM atonia index (RAI), the supra-threshold-REM-activity metric, the Frandsen index, the short/long muscle activity indices, and the Kempfner index algorithms were applied to 27 healthy control participants (C), 25 patients with Parkinson's disease (PD) without RBD (PD-RBD), 29 patients with PD and RBD (PD + RBD), 29 idiopathic patients with RBD, and 36 patients with periodic limb movement disorder (PLMD). The indices were calculated in various configurations: (1) considering all muscle activities; (2) excluding the ones related to arousals; (3) excluding the ones during apnea events; (4) excluding the ones before and after apnea events; (5) combining configurations 2 and 3; and (6) combining configurations 2 and 4. For each of these configurations, the discrimination capability of the indices was tested for the following comparisons: (1) (C, PD-RBD, PLMD) vs (PD + RBD, RBD); (2) C vs RBD; (3) PLMD vs RBD; (4) C vs PD-RBD; (5) C vs PLMD; (6) PD-RBD vs PD + RBD; and (7) C vs PLMD vs RBD. Results showed varying methods' performances across the different configurations and comparisons, making it impossible to identify the optimal method and suggesting the need of further improvements. Nevertheless, RAI seems the most sensible one for RBD detection. Moreover, apnea and arousal-related movements seem not to influence the algorithms' performances in patients' classification.

Validation of a new data-driven automated algorithm for muscular activity detection in REM sleep behavior disorder

“…To overcome these problems, several computerized methods have been developed and these include the REM atonia index (RAI) (Ferri et al, 2010(Ferri et al, , 2008, the supra-threshold REM activity metric (Burns et al, 2007), the short/long muscle activity index (Guttowski et al, 2018;Mayer et al, 2008), the Frandsen index (FRI) (Frandsen et al, 2015), the Kempfner index (KEI) and the computerized version of the SINBAR method (Frauscher et al, 2014). In a previous study, we compared the performances of these methods (except the computerized SINBAR due to lack of important implementation details in the original published description) in identifying RBD in different scenarios and we concluded that none of the methods could be elected as the optimal one, due to the varying performances of the methods across the different scenarios (Cesari et al, 2018b). However, we found that RAI, FRI and KEI achieved generally higher performances than the others (Cesari et al, 2018b).…”

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.…”

Clinical trials in REM sleep behavioural disorder: challenges and opportunities

“…Recent developments in ambulatory sleep monitoring devices and signal analysis methods may allow for measurement of RSWA at home over multiple nights or longitudinally. 114 There is no clearly superior method of RSWA quantification, 115 but consistent application of a single method-particularly one with multicentre comparisons and validations-would be required to use RSWA as an outcome marker.…”