Automatic 3D Video Analysis of Upper and Lower Body Movements to Identify Isolated REM Sleep Behavior Disorder: A Pilot Study*

Abstract Introduction The differentiation of isolated REM sleep behavior disorder (iRBD) or its prodromal phase (prodromal RBD, pRBD) from other disorders with motor activity during sleep is critical for identifying α-synucleinopathy in an early stage. Currently, definite RBD diagnosis requires video-polysomnography (vPSG). Aim of this study was to evaluate automated 3D video analysis of leg movements during REM sleep as objective diagnostic tool for iRBD. Methods A total of 122 participants (40 iRBD, 18 pRBD, 64 with other disorders with motor activity during sleep) were recruited among patients undergoing vPSG at the Sleep Disorders Unit, Department of Neurology, Medical University of Innsbruck. 3D videos synchronous to vPSG were recorded. Lower limb movements rate, duration, extent and intensity were computed using a newly developed software. Results The analyzed 3D movement features were significantly increased in subjects with iRBD compared to pRBD and other disorders with motor activity during sleep. Minor leg jerks with a duration <2 seconds discriminated with the highest accuracy (90.4%) iRBD from other motor activity during sleep. Automatic 3D analysis did not differentiate between pRBD and other disorders with motor activity during sleep. Conclusion Automated 3D video analysis of leg movements during REM sleep is a promising diagnostic tool for identifying subjects with iRBD in a sleep laboratory population and is able to distinguish iRBD from subjects with other motor activities during sleep. For future application as a screening, further studies should investigate usefulness of this tool when no information about sleep stages from vPSG is available. Support This study was funded by the Austrian Science Fund (FWF), Project KLI 677-B31.

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Automated 3D video analysis of lower limb movements during REM sleep: a new diagnostic tool for isolated REM sleep behavior disorder

SLEEP ◽

10.1093/sleep/zsaa100 ◽

2020 ◽

Vol 43 (11) ◽

Author(s):

Markus Waser ◽

Ambra Stefani ◽

Evi Holzknecht ◽

Bernhard Kohn ◽

Heinz Hackner ◽

...

Keyword(s):

Motor Activity ◽

Video Analysis ◽

Rem Sleep ◽

Diagnostic Tool ◽

Lower Limb ◽

Rem Sleep Behavior Disorder ◽

Behavior Disorder ◽

3D Video ◽

Sleep Behavior ◽

3D Video Analysis

Abstract Study Objectives The differentiation of isolated rapid eye movement (REM) sleep behavior disorder (iRBD) or its prodromal phase (prodromal RBD) from other disorders with motor activity during sleep is critical for identifying α-synucleinopathy in an early stage. Currently, definite RBD diagnosis requires video polysomnography (vPSG). The aim of this study was to evaluate automated 3D video analysis of leg movements during REM sleep as objective diagnostic tool for iRBD. Methods A total of 122 participants (40 iRBD, 18 prodromal RBD, 64 participants with other disorders with motor activity during sleep) were recruited among patients undergoing vPSG at the Sleep Disorders Unit, Department of Neurology, Medical University of Innsbruck. 3D videos synchronous to vPSG were recorded. Lower limb movements rate, duration, extent, and intensity were computed using a newly developed software. Results The analyzed 3D movement features were significantly increased in subjects with iRBD compared to prodromal RBD and other disorders with motor activity during sleep. Minor leg jerks with a duration < 2 seconds discriminated with the highest accuracy (90.4%) iRBD from other motor activity during sleep. Automatic 3D analysis did not differentiate between prodromal RBD and other disorders with motor activity during sleep. Conclusions Automated 3D video analysis of leg movements during REM sleep is a promising diagnostic tool for identifying subjects with iRBD in a sleep laboratory population and is able to distinguish iRBD from subjects with other motor activities during sleep. For future application as a screening, further studies should investigate usefulness of this tool when no information about sleep stages from vPSG is available and in the home environment.

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