pyActigraphy.sleep.ScoringMixin.Roenneberg_AoT

ScoringMixin.Roenneberg_AoT(trend_period='24h', min_trend_period='12h', threshold=0.15, min_seed_period='30Min', max_test_period='12h', r_consec_below='30Min', rsfreq=None)[source]

Automatic identification of activity onset/offset times, based on Roenneberg’s algorithm.

Identification of the activity onset and offset times using the algorithm for automatic identification of consolidated sleep episodes developped by Roenneberg et al. [1].

Parameters

  • trend_period (str, optional) – Time period of the rolling window used to extract the data trend. Default is ‘24h’.

  • min_trend_period (str, optional) – Minimum time period required for the rolling window to produce a value. Values default to NaN otherwise. Default is ‘12h’.

  • threshold (float, optional) – Fraction of the trend to use as a threshold for sleep/wake categorization. Default is ‘0.15’

  • min_seed_period (str, optional) – Minimum time period required to identify a potential sleep onset. Default is ‘30Min’.

  • max_test_period (str, optional) – Maximal period of the test series. Default is ‘12h’

  • r_consec_below (str, optional) – Time range to consider, past the potential correlation peak when searching for the maximum correlation peak. Default is ‘30Min’.

  • rsfreq (str, optional) – Resampling frequency used to evaluate the sleep periods. The final time series with rest/activity scores is returned with a frequency equal to one of the input data. If set to None, no resampling is performed. Default is None.

Returns

aot – Arrays containing the estimated activity onset and offset times, respectively.

Return type

(ndarray, ndarray)

Notes

Warning

The performance of this algorithm has been evaluated for actigraphy data aggregated in 10-min bins [2].

References

1

Roenneberg, T., Keller, L. K., Fischer, D., Matera, J. L., Vetter, C., & Winnebeck, E. C. (2015). Human Activity and Rest In Situ. In Methods in Enzymology (Vol. 552, pp. 257-283). http://doi.org/10.1016/bs.mie.2014.11.028

2

Loock, A., Khan Sullivan, A., Reis, C., Paiva, T., Ghotbi, N., Pilz, L. K., Biller, A. M., Molenda, C., Vuori‐Brodowski, M. T., Roenneberg, T., & Winnebeck, E. C. (2021). Validation of the Munich Actimetry Sleep Detection Algorithm for estimating sleep–wake patterns from activity recordings. Journal of Sleep Research, April, 1–12. https://doi.org/10.1111/jsr.13371

Examples