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

Grégory Hammad; Mathilde Reyt; Nikita Beliy; Marion Baillet; Michele Deantoni; Alexia Lesoinne; Vincenzo Muto; Christina Schmidt

doi:10.1371/journal.pcbi.1009514

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

PLoS Comput Biol. 2021 Oct 19;17(10):e1009514. doi: 10.1371/journal.pcbi.1009514. eCollection 2021 Oct.

Authors

Grégory Hammad¹, Mathilde Reyt^{1

2}, Nikita Beliy¹, Marion Baillet¹, Michele Deantoni¹, Alexia Lesoinne¹, Vincenzo Muto¹, Christina Schmidt^{1

2}

Affiliations

¹ GIGA-CRC In vivo Imaging, University of Liège, Liège, Belgium.
² Psychology and Neuroscience of Cognition, Faculty of Psychology, University of Liège, Liège, Belgium.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Actigraphy / methods*
Algorithms
Computational Biology / methods*
Data Visualization*
Humans
Signal Processing, Computer-Assisted
Sleep / physiology
Software*

Grants and funding

The development of the pyActigraphy package is part of the CogNap project that has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme, Grant agreement No. 757763 (to CS). This work was also supported by the Fonds de la Recherche Scientifique - FNRS under Grant nr T.0220.20 (to CS). CS is a research associate and MD is a FRIA grantee of the Fonds de la Recherche Scientifique - FNRS, Belgium (https://www.frs-fnrs.be/fr/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.