pyActigraphy.metrics.MetricsMixin.IS

MetricsMixin.IS(freq='1H', binarize=True, threshold=4)[source]

Interdaily stability

The Interdaily stability (IS) quantifies the repeatibilty of the daily rest-activity pattern over each day contained in the activity recording.

Parameters

  • freq (str, optional) – Data resampling frequency string. Default is ‘1H’.

  • binarize (bool, optional) – If set to True, the data are binarized. Default is True.

  • threshold (int, optional) – If binarize is set to True, data above this threshold are set to 1 and to 0 otherwise. Default is 4.

Returns

is

Return type

float

Notes

This variable is defined in ref [1]:

\[IS = \frac{d^{24h}}{d^{1h}}\]

with:

\[d^{1h} = \sum_{i}^{n}\frac{\left(x_{i}-\bar{x}\right)^{2}}{n}\]

where \(x_{i}\) is the number of active (counts higher than a predefined threshold) minutes during the \(i^{th}\) period, \(\bar{x}\) is the mean of all data and \(n\) is the number of periods covered by the actigraphy data and with:

\[d^{24h} = \sum_{i}^{p} \frac{ \left( \bar{x}_{h,i} - \bar{x} \right)^{2} }{p}\]

where \(\bar{x}^{h,i}\) is the average number of active minutes over the \(i^{th}\) period and \(p\) is the number of periods per day. The average runs over all the days.

For the record, tt is the 24h value from the chi-square periodogram (Sokolove and Bushel1 1978).

References

1

Witting W., Kwa I.H., Eikelenboom P., Mirmiran M., Swaab D.F. Alterations in the circadian rest–activity rhythm in aging and Alzheimer׳s disease. Biol Psychiatry. 1990;27:563–572.

Examples

>>> import pyActigraphy
>>> rawAWD = pyActigraphy.io.read_raw_awd(fpath + 'SUBJECT_01.AWD')
>>> rawAWD.IS()
0.6900175913031027
>>> rawAWD.IS(freq='30min', binarize=True, threshold=4)
0.6245582891144925
>>> rawAWD.IS(freq='1H', binarize=False)
0.5257020914453097