A Personalized and Adaptive Distribution Classification of Actigraphy Segments into Sleep-Wake States

Wearable actimeters have the potential to greatly improve our understanding sleep in natural environments and in long-term experiments. Current technologies have served the sleep community well, but they have known weaknesses that introduce errors that can compromise reliable and relevant clinical and research sleep and wakefulness profiles from these data. Newer data collection technologies, such as microelectromechanical systems (MEMS), offer opportunities to gather movement data in different forms and at higher frequencies, making new analytical methods possible and potentially advantageous. We have developed a novel statistical algorithm, called the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW), that is based on optimal transport statistics and uses MEMS data as its input. WACSAW segments group data into periods with similar movement patterns and uses optimal transport methods to generate a Wasserstein profile for each segment. The second utilization of optimal transport methodology measures the difference between each segment profile and a hypothetical segment of idealized sleep. Characteristic functions, derived from individual activity segments, were clustered and classified the segment as sleep or wakefulness. WACSAW was initially developed on a 6-person cohort and applied to an additional 16 independent participants. WACSAW returned >95% overall accuracy in sleep and wakefulness assignments validated against participant logs. Compared to the Actiwatch Spectrum Plus, WACSAW delivered a [~]10% improvement in accuracy, sensitivity, and selectivity and showed a reduced standard error between participants, indicating WACSAW conformed to individualized data. In addition, we directly compared WACSAW to GGIR, a current used algorithm designed to accept MEMS data. WACSAW showed an improvement in overall statistics and handled the time series and segmentation differently, which may contribute unique information to activity recordings. Here, we provide novel statistical approach to actimetry that improves sleep/wakefulness designations, adapts to individuals, provides interim metrics that further interpretations and is open source for modification. Author summaryWearables are an emerging class of technologies that have the potential to provide real-time, important information to individuals based on their unique biological and behavioral makeup. For 40 years, actimetry has been applied to identifying sleep and wakefulness in natural living environments as sleep changes between laboratory and home environments. Yet, these valuable analyses have weaknesses that may compromise accuracy. Newer actimetry wearables can collect high frequency movement data and offer an opportunity for different types of analyses. We have developed a novel algorithm called the Wasserstein Algorithm for Classifying Sleep and Wakefulness (WACSAW). WACSAW employs optimal transport statistics to compare movement variation between different time segments to classify sleep or wakefulness for that segment. WACSAW produces >95% accuracy across the 24-hr day for both sleep and wakefulness categorization and more accurately classifies behavior than the Actiwatch Spectrum Plus. In addition, WACSAW output has interim metrics that can be used to assess reliability of the output and requires no human intervention to run. WACSAW may help achieve observations in daily living situations to determine factors that alter sleep, understand the variation in sleep, and set the stage for more home diagnoses and disease identification.