biopsykit.sleep.sleep_wake_detection.algorithms.sadeh module¶
Sleep/Wake detection using the Sadeh Algorithm.
- class biopsykit.sleep.sleep_wake_detection.algorithms.sadeh.Sadeh(**kwargs)[source]¶
Bases:
biopsykit.sleep.sleep_wake_detection.algorithms._base._SleepWakeBase
Class representing the Sadeh Algorithm for sleep/wake detection based on activity counts.
The Sadeh 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.
References
add reference
- 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
fit
step is not needed.- Parameters
data (array_like) – input data
- predict(data, **kwargs)[source]¶
Apply sleep/wake prediction algorithm on input data.
- Parameters
data (array_like) – array with activity index values
**kwargs –
additional arguments to be passed to the algorithm for prediction, such as:
rescore_data
(bool
):True
to apply Webster’s rescoring rules to the sleep/wake predictions,False
otherwise. Default:True
epoch_length
(int
): activity data epoch lengths in seconds, i.e. Epoch lengths are usually 30 or 60 seconds. Default: 60
- Returns
dataframe with sleep/wake predictions
- Return type