yasa.SWResults.compare_channels

SWResults.compare_channels(score='f1', max_distance_sec=0)

Compare detected slow-waves across channels.

This is a wrapper around the yasa.compare_detection function. Please refer to the documentation of this function for more details.

Parameters:

scorestr

The performance metric to compute. Accepted values are “precision”, “recall” (aka sensitivity) and “f1” (default). The F1-score is the harmonic mean of precision and recall, and is usually the preferred metric to evaluate the agreement between two channels. All three metrics are bounded by 0 and 1, where 1 indicates perfect agreement.

max_distance_secfloat

The maximum distance between slow-waves, in seconds, to consider as the same event.

Warning

To reduce computation cost, YASA rounds the start time of each spindle to the nearest decisecond (= 100 ms). This means that the lowest possible resolution is 100 ms, regardless of the sampling frequency of the data. Two slow-waves starting at 500 ms and 540 ms on their respective channels will therefore always be considered the same event, even when max_distance_sec=0.

Returns:

scorespandas.DataFrame

A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).

Notes

Some use cases of this function:

1. What proportion of slow-waves detected in one channel are also detected on another channel (if using score="recall").

2. What is the overall agreement in the detected events between channels?

3. Is the agreement better in channels that are close to one another?