pyActigraphy

Open-source python package for actigraphy data visualization and analysis.

This package is meant to provide a comprehensive set of tools to:

• read native actigraphy data files with various formats:
  • Actigraph: wGT3X-BT
  • CamNtech: Actiwatch 4 and MotionWatch 8
  • Condor Instrument: ActTrust 2
  • Daqtix: Daqtometer
  • Respironics: Actiwatch 2 and Actiwatch Spectrum (plus)
  • Tempatilumi (CE Brasil)

• clean the raw data and mask spurious periods of inactivity
• produce activity profile plots
• visualize sleep agendas and compute summary statistics
• calculate typical wake/sleep cycle-related variables:
  • Non-parametric rest-activity variables: IS(m), IV(m), RA
  • Activity or Rest fragmentation: kRA, kAR
  • Sleep regularity index (SRI)

• automatically detect rest periods using various algorithms (Cole-Kripke, Sadeh, ..., Crespo, Roenneberg)
• perform complex analyses:
  • Cosinor analysis
  • Detrended Fluctuation Analysis (DFA)
  • Functional Linear Modelling (FLM)
  • Locomotor Inactivity During Sleep (LIDS)
  • Singular Spectrum Analysis (SSA)
  • and much more...

Code and documentation

The pyActigraphy package is open-source and its source code is accessible online.

An online documentation of the package is also available here. It contains notebooks illustrating various functionalities of the package.

Installation

In a (bash) shell, simply type:

• For users:

pip3 install pyActigraphy

To update the package:

pip3 install -U pyActigraphy

• For developers:

git clone git@github.com:ghammad/pyActigraphy.git
cd pyActigraphy/
git checkout develop
pip3 install -e .

Quick start

The following example illustrates how to calculate the interdaily stability with the pyActigraphy package:

>>> import pyActigraphy
>>> rawAWD = pyActigraphy.io.read_raw_awd('/path/to/your/favourite/file.AWD')
>>> rawAWD.IS()
0.6900175913031027
>>> rawAWD.IS(freq='30min', binarize=True, threshold=4)
0.6245582891144925
>>> rawAWD.IS(freq='1H', binarize=False)
0.5257020914453097

Contributing

There are plenty of ways to contribute to this package, including (but not limiting to):

• report bugs (and, ideally, how to reproduce the bug)
• suggest improvements
• improve the documentation

Authors

• Grégory Hammad @ghammad - Initial and main developer
• Mathilde Reyt @ReytMathilde

See also the list of contributors who participated in this project.

License

This project is licensed under the GNU GPL-3.0 License - see the LICENSE file for details

Acknowledgments

• Aubin Ardois @aardoi developed the first version of the MTN class during his internship at the CRC, in May-August 2018.
• The CRC colleagues for their support, ideas, etc.