pyActigraphy: Open-source python package for actigraphy data visualization and analysis

Abstract

Over the past 40 years, actigraphy has been used to study rest-activity patterns in circadian rhythm and sleep research. Furthermore, considering its simplicity of use, there is a growing interest in the analysis of large population-based samples, using actigraphy. Here, we introduce pyActigraphy, a comprehensive toolbox for data visualization and analysis including multiple sleep detection algorithms and rest-activity rhythm variables. This open-source python package implements methods to read multiple data formats, quantify various properties of rest-activity rhythms, visualize sleep agendas, automatically detect rest periods and perform more advanced signal processing analyses. The development of this package aims to pave the way towards the establishment of a comprehensive open-source software suite, supported by a community of both developers and researchers, that would provide all the necessary tools for in-depth and large scale actigraphy data analyses.

Fig 1. Diagram of the code architecture.

Fig 2. Visualization example of average daily profiles obtained with pyActigraphy using example files included in the package.

Fig 3. Visualization example of actigraphy data, overlaid with periods (green: Nap, grey: Night, red: Device not worn) reported in the sleep diary example file included in the package.

Fig 4. Visualization example of actigraphy data, overlaid with periods scored as “active” (0) or “rest” (1) by Roenneberg’s algorithm [33] for two different settings (full line: Default parameter values, dash line: With a threshold set at 0.25 of the activity trend).