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0678 Contactless 3D Detection Of Leg Movements In Sleep

2018, Sleep

Abstract

Methods: All participants underwent a polysomnogram (PSG). The apnea hypopnea index (AHI), total sleep time (TST), Sleep Efficiency (SE), Sleep Onset Latency (SOL), arousals and PLM Index (PLMI) were computed. Results: Of all subjects, 19.7% of the participants had PLMI ≥ 10/hour, 14.8% had PLMI ≥ 15/hour, 12.1% had PLMI ≥ 20/hour and 7.5% had PLMI ≥ 30/hour. The 75 th percentile PLMI was 5.5, 80th percentile was 9.3, 90th percentile was 24.1 and 95th percentile was 37.2/hour. PLMI was associated positively with SOL (R=.075, P=0.01) and inversely with SE (R=-.113, P=<0.001) and TST (R=-.106, P=<0.001). Linear regression models showed that the association between PLMI and sleep variables was independent of AHI and depression (HAMD score). There was no significant correlation between PLMI and AHI, Epworth Sleepiness Scale scores or Maintenance of Wakefulness Test sleep latency. No correlation was seen between PLMI and Hamilton Depression Rating Scale (HAMD) or Sleep Apnea Quality of Life Index (SAQLI) scores. A linear regression model showed increasing age (Beta=.19, P<0.01) and total caffeine servings per week (Beta=.09, P=0.02) to be independent predictors of PLMI. A logistic regression model showed higher odds of PLMI ≥ 10 with older age (OR=1.03, P<0.001), male gender (OR=1.63, P=0.01), antidepressant use (OR=1.48, P=0.048), and caffeine servings per week (OR=1.