P 120 A machine learning approach to detecting sleep and sleep disorders in acceleration sensor data

The major diagnostic sleep laboratory tool for assessing excessive daytime sleepiness (EDS), the multiple sleep latency test (MSLT), is increasingly criticized for poor precision in the differentiation of idiopathic hypersomnia (IH) and narcolepsy (Trotti et al., 2013; Johns, 2000). Recent evidence suggests that actigraphy can supplement the diagnostic process by providing information about the sleep-wake rhythm (Kretzschmar et al., 2016; Filardi et al., 2015; Bruck et al., 2005). An actigraphy analysis tool is introduced that processes actigraphy recordings with machine learning methods.

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