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Abstract
Background: Unlike other episodic sleep disorders in childhood, there are no agreed severity indices for rhythmic movement disorder. While movements can be characterized in detail by polysomnography, in our experience most children inhibit rhythmic movement during polysomnography. Actigraphy and home video allow assessment in the child’s own environment, but both have limitations. Standard actigraphy analysis algorithms fail to differentiate rhythmic movements from other movements. Manual annotation of 2D video is time consuming. We aimed to develop a sensitive, reliable method to detect and quantify rhythmic movements using marker free and automatic 3D video analysis. Method: Patients with rhythmic movement disorder (n = 6, 4 male) between age 5 and 14 years (M: 9.0 years, SD: 4.2 years) spent three nights in the sleep laboratory as part of a feasibility study (https://clinicaltrials.gov/ct2/show/NCT03528096). 2D and 3D video data recorded during the adaptation and baseline nights were analyzed. One ceiling-mounted camera captured 3D depth images, while another recorded 2D video. We developed algorithms to analyze the characteristics of rhythmic movements and built a classifier to distinguish between rhythmic and non-rhythmic movements based on 3D video data alone. Data from 3D automated analysis were compared to manual 2D video annotations to assess algorithm performance. Novel indices were developed, specifically the rhythmic movement index, frequency index, and duration index, to better characterize severity of rhythmic movement disorder in children. Result: Automatic 3D video analysis demonstrated high levels of agreement with the manual approach indicated by a Cohen’s kappa >0.9 and F1-score >0.9. We also demonstrated how rhythmic movement assessment can be improved using newly introduced indices illustrated with plots for ease of visualization. Conclusion: 3D video technology is widely available and can be readily integrated into sleep laboratory settings. Our automatic 3D video analysis algorithm yields reliable quantitative information about rhythmic movements, reducing the burden of manual scoring. Furthermore, we propose novel rhythmic movement disorder severity indices that offer a means to standardize measurement of this disorder in both clinical and research practice. The significance of the results is limited due to the nature of a feasibility study and its small number of samples. A larger follow up study is needed to confirm presented results.
Highlights
Rhythmic movement disorder (RMD) is a poorly understood sleep-related movement disorder defined by the International Classification of Sleep Disorders (ICSD-III) [1] as repetitive, stereotyped rhythmic movements (RMs) of large muscle groups in the frequency range 0.5–2 Hz [1], [2]
We aimed to address these limitations by evaluating a novel automated 3D assessment algorithm to quantify RMs
We aimed to develop novel indexes to quantify RMD severity as a basis for future international standards of assessment in this poorly understood disorder
Summary
Rhythmic movement disorder (RMD) is a poorly understood sleep-related movement disorder defined by the International Classification of Sleep Disorders (ICSD-III) [1] as repetitive, stereotyped rhythmic movements (RMs) of large muscle groups in the frequency range 0.5–2 Hz [1], [2]. A diagnosis of RMD is only made when there are clinical consequence of nocturnal movements, significant sleep disturbance, impaired daytime functioning, or physical injury [1]. RMD can have social consequences; it may cause embarrassment [6, 7] and noise from head-banging or movements of the bed may disturb other household members [8]. Limited studies report poor concentration, difficult behavior, as well as impaired memory and decision making capabilities in children with this condition [9, 10]. Standard actigraphy analysis algorithms fail to differentiate rhythmic movements from other movements. We aimed to develop a sensitive, reliable method to detect and quantify rhythmic movements using marker free and automatic 3D video analysis
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