pyActigraphy.sleep.ScoringMixin.Roenneberg

ScoringMixin.Roenneberg(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 sleep detection.

Identification of consolidated sleep episodes using the algorithm 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

rbg – Time series containing the estimated periods of rest (1) and activity (0).

Return type

pandas.core.Series

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