pyActigraphy.io.BaseRaw.Scripps

BaseRaw.Scripps(scale=0.204, window=array([0.0064, 0.0074, 0.0112, 0.0112, 0.0118, 0.0118, 0.0128, 0.0188, 0.028, 0.0664, 0.03, 0.0112, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), threshold=1.0)

Scripps Clinic algorithm for sleep-wake identification.

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

Parameters

• scale (float, optional) – Scale parameter P Default is 0.204.

• window (np.array, optional) – Array of weighting factors \(W_{i}\) Default values are identical to those found in the original publication [1].

• threshold (float, optional) – Threshold value for scoring sleep/wake. Default is 1.0.

Returns

scripps – Time series containing the D scores (0: sleep, 1: wake) for each epoch.

Return type

pandas.core.Series

Notes

The output variable D of the Scripps algorithm is defined in [1] as:

\[D = P*( [W_{-10},\dots,W_{0},\dots,W_{+10}] \cdot [A_{-10},\dots,A_{0},\dots,A_{+10}])\]

with:

• D < 1 == sleep, D >= 1 == wake;

• P, scale factor;

• \(W_{0},W_{-1},W_{+1},\dots\), weighting factors for the present epoch, the previous epoch, the following epoch, etc.;

• \(A_{0},A_{-1},A_{+1},\dots\), activity scores for the present epoch, the previous epoch, the following epoch, etc.

References

1(1,2,3)

Kripke, D. F., Hahn, E. K., Grizas, A. P., Wadiak, K. H., Loving, R. T., Poceta, J. S., … Kline, L. E. (2010). Wrist actigraphic scoring for sleep laboratory patients: algorithm development. Journal of Sleep Research, 19(4), 612–619. http://doi.org/10.1111/j.1365-2869.2010.00835.x