yasa.SWResults.find_cooccurring_spindles#
- SWResults.find_cooccurring_spindles(spindles, lookaround=1.2)[source]#
Given a spindles detection summary dataframe, find slow-waves that co-occur with sleep spindles.
Added in version 0.6.0.
- Parameters:
- spindles
pandas.DataFrame Output dataframe of
yasa.SpindlesResults.summary.- lookaroundfloat
Lookaround window, in seconds. The default is +/- 1.2 seconds around the negative peak of the slow-wave, as in [1]. This means that YASA will look for a spindle in a 2.4 seconds window centered around the downstate of the slow-wave.
- spindles
- Returns:
- _events
pandas.DataFrame The slow-wave detection is modified IN-PLACE (see Notes). To see the updated dataframe, call the
yasa.SWResults.summarymethod.
- _events
Notes
From [1]:
“SO–spindle co-occurrence was first determined by the number of spindle centers occurring within a ±1.2-sec window around the downstate peak of a SO, expressed as the ratio of all detected SO events in an individual channel.”
This function adds three columns to the output detection dataframe:
CooccurringSpindle: a boolean column (True / False) that indicates whether the given slow-wave co-occur with a sleep spindle.
CooccurringSpindlePeak: the timestamp of the peak of the co-occurring, in seconds from beginning of recording. Values are set to np.nan when no co-occurring spindles were found.
DistanceSpindleToSW: The distance in seconds from the center peak of the spindles and the negative peak of the slow-waves. Negative values indicate that the spindles occured before the negative peak of the slow-waves. Values are set to np.nan when no co-occurring spindles were found.
References