YASA (Yet Another Spindle Algorithm) : a fast and data-agnostic sleep spindle detection algorithm.
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YASA (Yet Another Spindle Algorithm) is a fast and data-agnostic sleep spindles detection algorithm written in Python 3.

The algorithm behind YASA is largely inspired by the method described in:

Lacourse, K., Delfrate, J., Beaudry, J., Peppard, P., Warby, S.C., 2018. A sleep spindle detection algorithm that emulates human expert spindle scoring. J. Neurosci. Methods. https://doi.org/10.1016/j.jneumeth.2018.08.014


pip install --upgrade yasa


  • numpy>=1.14
  • scipy>=1.1.0
  • pandas>=0.23,
  • mne>=0.17.0
  • numba>=0.39.0
  • scikit-learn>=0.20


Several functions of YASA are written using Numba, a just-in-time compiler for Python. This allows to greatly speed up the computation time (typically a few seconds for a full night recording).


Please refer to notebooks/spindles_detection.ipynb for an example on how to use YASA as well as a step-by-step description of the algorithm.

Typical use

import yasa
yasa.spindles_detect(data, sf)

The result of the detection is a pandas DataFrame

Start End Duration Amplitude RMS AbsPower RelPower Frequency Oscillations Symmetry
3.35 4.03 0.68 81.49 20.17 2.74 0.54 12.82 8 0.67
13.16 13.86 0.70 99.32 24.19 2.84 0.31 12.23 8 0.35

which can then be easily used to plot the detected spindles


Interactive visualization with Visbrain

YASA can also be used in combination with the Sleep module of the Visbrain visualization suite. That way, the result of the spindles detection can easily be displayed and checked in an interactive graphical user interface. To do so, load Visbrain using the following python file (make sure to update 'PATH/TO/EEGFILE').

from visbrain.gui import Sleep
from yasa import spindles_detect

sl = Sleep(data='PATH/TO/EEGFILE')

def fcn_spindle(data, sf, time, hypno):
    """Replace Visbrain built-in spindles detection by YASA algorithm.
    See http://visbrain.org/sleep.html#use-your-own-detections-in-sleep
    sp = spindles_detect(data, sf, freq_sp=(11, 16),
                         duration=(0.5, 2), freq_broad=(1, 30),
                         thresh={'rel_pow': 0.2, 'corr': 0.65, 'rms': 1.5})
    return (sp[['Start', 'End']].values * sf).astype(int)

sl.replace_detections('spindle', fcn_spindle)

Then navigate to the Detection tab and click on Apply to run the YASA algorithm on the specified channel.


Outlier rejection

YASA incorporates an optional post-processing step to identify and remove pseudo (fake) spindles. The method is based on a machine-learning algorithm (the Isolation Forest, implemented in the scikit-learn package), which uses the spindles parameters (e.g. amplitude, duration, frequency, etc) as input features to identify "abnormal" spindles.

To activate this post-processing step, simply use:

import yasa
yasa.spindles_detect(data, sf, remove_outliers=True)

As an example, the performance of YASA were compared on a ~8 hours recording in an healthy young adults. As shown below, the initial detection - i.e. without the outlier rejection - returned 840 spindles. After outlier removal, the number of spindles was down to 710, meaning that 130 spindles were considered outliers and removed from the dataframe.



YASA was created and is maintained by Raphael Vallat. Contributions are more than welcome so feel free to contact me, open an issue or submit a pull request!

To see the code or report a bug, please visit the GitHub repository.

Note that this program is provided with NO WARRANTY OF ANY KIND.


To cite YASA, please use the Zenodo DOI: