pyActigraphy.io.BaseRaw.Sadeh

BaseRaw.Sadeh(offset=7.601, weights=array([-0.065, -1.08, -0.056, -0.703]), threshold=0.0)

Sadeh algorithm for sleep identification

Algorithm for automatic sleep scoring based on wrist activity, developped by Sadeh et al [1].

Parameters

  • offset (float, optional) – Offset parameter. Default is 7.601.

  • weights (np.array) – Array of weighting factors for mean_W5, NAT, sd_Last6 and logAct. Default is [-0.065, -1.08, -0.056, -0.703].

  • threshold (float, optional) – Threshold value for scoring sleep/wake. Default is 0.0.

Notes

The output variable PS of the Sadeh algorithm is defined in [2] as:

\[PS = 7.601-0.065·mean_W5-1.08·NAT-0.056·sd_Last6-0.703·logAct\]

with:

  • PS >= 0 == sleep, PS < 0 == wake;

  • mean_W5, the average number of activity counts during the scored epoch and the window of five epochs preceding and following it;

  • sd_Last6, the standard deviation of the activity counts during the scored epoch and the five epochs preceding it;

  • NAT, the number of epochs with activity level equal to or higher than 50 but lower than 100 activity counts in a window of 11 minutes that includes the scored epoch and the five epochs preceding and following it;

  • logAct, the natural logarithm of the number of activity counts during the scored epoch + 1.

References

1

Sadeh, A., Alster, J., Urbach, D., & Lavie, P. (1989). Actigraphically based automatic bedtime sleep-wake scoring: validity and clinical applications. Journal of ambulatory monitoring, 2(3), 209-216.

2

Sadeh, A., Sharkey, M., & Carskadon, M. A. (1994). Activity-Based Sleep-Wake Identification: An Empirical Test of Methodological Issues. Sleep, 17(3), 201–207. http://doi.org/10.1093/sleep/17.3.201

Examples