Actant - Activity Analysis Toolbox

Copyright (C) 2013, Maxim Osipov maxim.osipov@gmail.com Copyright (C) 2013-2015, Bart te Lindert b.te.lindert@nin.knaw.nl

This toolbox include multiple data analysis algorithms implemented in Matlab and released under Open Source licences. Respective licences are referenced directly in the code of specific Matlab functions.

About

Actant provides functionality for visualization and analysis of behavioral and environmental timeseries, acquired using Actiwatch-L and GENEActiv accelerometers as well as Actopsy mobile application. Currently implemented analysis methods include:

• Non-parametric activity analysis:

  • Van Someren EJW, et al. "Bright light therapy: improved sensitivity to its effects on rest-activity rhythms in Alzheimer patients by application of nonparametric methods." Chronobiology international 1999; 16(4):505-518.

• Sleep analysis:

  • Oakley NR. "Validation with polysomnography of the Sleepwatch sleep/wake scoring algorithm used by the Actiwatch activity monitor system: Technical Report to Mini-Mitter Co., Inc., 1997.

  • Kushida CA, Chang A, Gadkary C, Guilleminault C, Carrillo O, Dement WC. "Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients." Sleep Medicine 2001; 2:389-96.

  • te Lindert BHW, Van Someren EJW. "Sleep estimates using microelectro- mechanical systems (MEMS)." SLEEP 2013;36(5):781-789

• Sample Entropy:

  • Richman, Joshua S, Randall Moorman J. "Physiological time-series analysis using approximate entropy and sample entropy." American Journal of Physiology-Heart and Circulatory Physiology 2000; 278(6):H2039-H2049.

• Multiscale Entropy:

  • Costa, Madalena, Goldberger AL, Peng CK. "Multiscale entropy analysis of complex physiologic time series." Physical review letters 2002; 89(6):068102.

The toolbox provides flexible interface for integration of both new data formats and analysis methods.

Data formats

Actant reads data in Actiwatch, GENEActiv, Actopsy app (CSV) and Actant own (MAT) formats. Internally, all datasets represented as Matlab timeseries objects with the following names:

• ACC - 3D accelerometry data (m/s^2 or g)
• ACT - activity data (counts or m/s^2 or g)
• LIGHT - light data (lux)
• TEMP - temperature data (degC)
• BUTTON - button press indicators (binary)
• SPEED - distance travelled (km/h, inaccurate depending on location)
• TEXTS - sms messages (days, each character counted as second)
• CALLS - phone calls (days)

Plus, in future:

• GYRO - 3D gyroscope (deg)
• HUM - humidity (%)
• PRES - atmospheric pressure (mbar)
• HR - heart rate (bpm)

Other timeseries are possible with specific measures, for example mood ratings from questionnaires.

Data load methods have the following interface:

function ts = load_format(file)
  Arguments:
    file - File name

  Results:
    ts - Structure of time series

Additionally, markup timeseries can be generated using analysis algorithms, where time vector represents starting points of marked intervals and data vector represents end points (as datenum).

- markup data (days)

For unification of data analysis, all high-dimensional and high-sampling rate activity data can be converted to epochs, either Actiwatch compatible (counts) or as average acceleration per epoch (m/s^2).

Actant MAT format includes Matlab variables to initialize Actant internal state, including datasets, analysis algorithm and results:

• actant_datasets - cell array of timeseries objects for datasets
• actant_sources - cell array of sources (files or analysis) for 'datasets'
• actant_plot - values for display characteristics
• actant_analysis
  • method - analysis method name (matlab function name)
  • args - cell array method arguments
  • results - cell array results of analysis (except timeseries)

Analysis methods

Analysis methods may generate scalar values and timeseries (with values or data markup). For example, non-linear activity analysis will generate L5, M10, RA, IS and IV values plus timeseries with segmentations of L5 and M10. Windowed spectral analysis will generate timeseries with spectral content of analyzed signal.

The API of analysis method is as following:

function [ts vals] = actant_analysis(data, args)
  Arguments:
    data - Input data timeseries
    args - Cell array of arguments

  Results (all optional):
    ts - Cell array of timeseries
    vals - Cell array of results

When method called without arguments, array of function arguments and default values is returned in vals, where the first element of array is '_' with the name of the analysis method as value.

Visualization

The main plot is N days activity plot and can be overlapped with additional plot (YY axis, result of windowed analysis) and with markup (transparent patches, result of segmentation).

The plot is configurable with a number of plots to display, number of days per plot and overlap between plots (in days), so that both single day plots can be displayed with N-1-0 and 48 hours actogram with N-2-1 settings.

Contributions

Authors would appreciate your contributions to the Actant project (code can be found at https://github.com/maximosipov/actant). Also, you are invited to contrubute your datasets to Physionet (http://www.physionet.org/).