Video analysis of motor events in REM sleep behavior disorder
ABSTRACT In REM sleep behavior disorder (RBD), several studies focused on electromyographic characterization of motor activity, whereas video analysis has remained more general. The aim of this study was to undertake a detailed and systematic video analysis. Nine polysomnographic records from 5 Parkinson patients with RBD were analyzed and compared with sex- and age-matched controls. Each motor event in the video during REM sleep was classified according to duration, type of movement, and topographical distribution. In RBD, a mean of 54 ± 23.2 events/10 minutes of REM sleep (total 1392) were identified and visually analyzed. Seventy-five percent of all motor events lasted <2 seconds. Of these events, 1,155 (83.0%) were classified as elementary, 188 (13.5%) as complex behaviors, 50 (3.6%) as violent, and 146 (10.5%) as vocalizations. In the control group, 3.6 ± 2.3 events/10 minutes (total 264) of predominantly elementary simple character (n = 240, 90.9%) were identified. Number and types of motor events differed significantly between patients and controls (P < 0.05). This study shows a very high number and great variety of motor events during REM sleep in symptomatic RBD. However, most motor events are minor, and violent episodes represent only a small fraction. © 2007 Movement Disorder Society
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ABSTRACT: Objective To investigate demography and clinic and polysomnographic characteristics in Chinese rapid eye movement (REM) sleep behavior disorder (RBD) patients across onset ages. Methods Ninety consecutive patients fulfilling the criteria for RBD were recruited for study in our sleep center. Patients were separated into early- and late-onset groups according to age when symptoms began (⩽50 and >50 years, respectively). Ninety age- and gender-matched healthy subjects served as controls. All subjects were interviewed for their clinical history, completed an RBD questionnaire and underwent an overnight video polysomnography assessment. Demographics, comorbidities, scores on the RBD questionnaire, sleep architecture and EMG activity were compared between the patients and controls and between the early- and late-onset groups. Results Of all RBD patients, 63 were male, and mean age of RBD onset was 54.3 ± 15.7 years. In 25 patients (28%), RBD was secondary and associated with neurodegenerative disease, narcolepsy or antidepressant use. Twenty-three patients (26%) had early-onset RBD and 67 (74%) were in the late-onset group. RBD patients had significantly more comorbidities, dreams and dream-enacting behaviors, and poorer sleep quality than did controls. The early-onset group had a high proportion of females (48%) and an increased proportion of cases associated with narcolepsy. The early-onset group also had fewer movements, lower EMG activity during REM sleep, and better sleep quality when compared to the late-onset group. EMG activity was positively correlated with age of onset. The mean follow-up time was 1.57 ± 0.82 years and four patients in the late-onset group were subsequently diagnosed with neurodegenerative diseases. Conclusions Stratifying patients into early and late-onset RBD revealed different characteristics from those previously described as typical for RBD. EMG activity during REM sleep was positively correlated with age of onset. We suggest that it will be valuable to explore the relationship between age of onset conversion and neurodegenerative diseases.Sleep Medicine 06/2014; 15(6). DOI:10.1016/j.sleep.2013.12.020 · 3.10 Impact Factor
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ABSTRACT: Actigraphy is effective at monitoring circadian rhythms, but often misidentifies periods of restless sleep (defined here as sleep periods with movement) as wake, and periods of quiet wake as sleep. This limitation restricts the effectiveness of actigraphy for investigating sleep disorders. Our objective in this study was to investigate a time-frequency representation of movement during sleep and wake which could ultimately aid in improving classification performance by reducing false wake detections. As a pilot study, we investigate the characteristics of manually labelled movements from six patients (aged 6-12 years, 3 male) during sleep and wake using the over complete discrete wavelet decomposition. The difference between the median wavelet coefficients were analyzed for 30 movement segments from six movement categories during sleep and wake. We found that, in general, the temporal location of high energy coefficients and the energy of the high frequency bands differed between movements during sleep and wake. This indicates that we are able to differentiate movement during sleep and wake with a time-frequency representation. This representation may improve the sleep and wake classification performance by identifying movements specific to sleep and wake. This will likely improve the poor specificity inherent in conventional actigraphy.
Sleep Medicine 09/2014; 15(9). DOI:10.1016/j.sleep.2014.05.006 · 3.10 Impact Factor