Multisite accelerometry for sleep and wake classification in children

Actigraphy is a useful alternative to the gold standard polysomnogram for non-invasively measuring sleep and wakefulness. However, it is unable to accurately assess sleep fragmentation due to its inability to differentiate restless sleep from wakefulness and quiet wake from sleep. This presents significant limitations in the assessment of sleep-related breathing disorders where sleep fragmentation is a common symptom. We propose that this limitation may be caused by hardware constraints and movement representation techniques. Our objective was to determine if multisite tri-axial accelerometry improves sleep and wake classification. Twenty-four patients aged 6-15 years (median: 8 years, 16 male) underwent a diagnostic polysomnogram while simultaneously recording motion from the left wrist and index fingertip, upper thorax and left ankle and great toe using a custom accelerometry system. Movement was quantified using several features and two feature selection techniques were employed to select optimal features for restricted feature set sizes. A heuristic was also applied to identify movements during restless sleep. The sleep and wake classification performance was then assessed and validated against the manually scored polysomnogram using discriminant analysis. Tri-axial accelerometry measured at the wrist significantly improved the wake detection when compared to uni-axial accelerometry (specificity at 85% sensitivity: 71.3(14.2)% versus 55.2(24.7)%, p < 0.01). Multisite accelerometry significantly improved the performance when compared to the single wrist placement (specificity at 85% sensitivity: 82.1(12.5)% versus 71.3(14.2)%, p < 0.05). Our results indicate that multisite accelerometry offers a significant performance benefit which could be further improved by analysing movement in raw multisite accelerometry data.