biopsykit.sleep.sleep_wake_detection.algorithms.cole_kripke_old module¶
Sleep/Wake detection using the Cole/Kripke Algorithm.
- class biopsykit.sleep.sleep_wake_detection.algorithms.cole_kripke_old.ColeKripkeOld(**kwargs)[source]¶
Bases:
biopsykit.sleep.sleep_wake_detection.algorithms._base._SleepWakeBaseClass representing the Cole/Kripke Algorithm for sleep/wake detection based on activity counts.
The Cole/Kripke Algorithm runs sleep wake detection on epoch level activity data. Epochs are 1 minute long and activity is represented by an activity index which comes from Actigraph data or from raw acceleration data converted into activity index data.
- Parameters
scale_factor (float) – scale factor to use for the predictions (default corresponds to scale factor optimized for use with the activity index, if other activity measures are desired the scale factor can be modified or optimized.) The recommended range for the scale factor is between 0.1 and 0.25 depending on the sensitivity to activity desired, and possibly the population being observed.
References
Cole, R. J., Kripke, D. F., Gruen, W., Mullaney, D. J., & Gillin, J. C. (1992). Automatic Sleep/Wake Identification From Wrist Activity. Sleep, 15(5), 461-469. https://doi.org/10.1093/sleep/15.5.461
- scale_factor: float¶
Scale factor to use for the predictions (default corresponds to scale factor optimized for use with the activity index, if other activity measures are desired the scale factor can be modified or optimized). The recommended range for the scale factor is between 0.1 and 0.25 depending on the sensitivity to activity desired, and possibly the population being observed.
- fit(data, **kwargs)[source]¶
Fit sleep/wake detection algorithm to input data.
Note
Algorithms that do not have to (re)fit a ML model before sleep/wake prediction, such as rule-based algorithms, will internally bypass this method as the
fitstep is not needed.- Parameters
data (array_like) – input data
- Return type
biopsykit.utils._types.arr_t