biopsykit.sleep.sleep_wake_detection.algorithms.sazonov module

Sleep/Wake detection using the Sazonov Algorithm.

class biopsykit.sleep.sleep_wake_detection.algorithms.sazonov.Sazonov(**kwargs)

Bases: biopsykit.sleep.sleep_wake_detection.algorithms._base._SleepWakeBase

Class representing the Sazonov Algorithm for sleep/wake detection based on activity counts.

The Sazonov 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)

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)

Apply sleep/wake prediction algorithm on input data.

Parameters

• data (array_like) – input data 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: 30

Returns

dataframe with sleep/wake predictions

Return type

SleepWakeDataFrame

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