Detect REM events with YASA

This tool utilizes the Yet Another Splindle Algorithm (YASA) rapid eye movments (REMs) detector algorithm.

Algorithm Details

This algorithm uses the idea from the methodologies proposed in [1] and [2], primarily building upon Agwal’s [2] approach, which applies amplitude thresholding to the negative product of the LOC and ROC filtered signals. Using this technique, the algorithm identifies signal peaks and extracts key features, including the duration of the REM period, the peak absolute values of ROC and LOC, as well as the absolute rise and fall slopes for both signals.

To ensure reliable performance, the algorithm requires a minimum of 50 detected REMs to apply its model, which is based on the IsolationForest random forest classifier. Additionally, if the user selects the “remove outlier” option as True, any outliers detected after applying the IsolationForest will be excluded from the final detection results.

Note

Only REMs events that fall within at least one-third of the REM sleep stage are retained. Events occurring outside this threshold are discarded. (This rule is important when a REM event is detected at the transition of two sleep stages.)

- Usage points:

  • All output parameters of this algorithm are computed using the filtered LOC and ROC signals. The filtering process is based on the thresholds defined in the DetectionStep.

  • For optimal results, the user should apply this detection only to artifact-free REM sleep data.

The example of the REMs events in two EOG channels left and right are shown in Figure 1.

Example of REMs events

Figure 1: The example of REMs events in two EOG channels left and right

Input

PSG files including header and events are needed.

  • European Data Format (EDF) :

    The corresponding .tsv file is required. Both files must be saved in the same directory and share the exact same filename.

  • Stellate format (up to version 6.2) :

    The corresponding .sig file is required with the .sts. Both files must be saved in the same directory and share the exact same filename.

  • NATUS format (version 9.1) :

    (CEAMS users only) The entire NATUS subject folder is required.

For more details on accepted formats, see Polysomnography file format.

The annotations files have to include the sleep staging.

Channels: This tool requires the selection of 2 EOG channels per recording for REMs detection.

Output

The REMs detected events are added in the annotations files (.tsv, .sts or .ent) depending of the format used. If the annotations file already includes the group event to be added, the existing entries will be removed before adding the new ones.

Each events is defined by :

  1. group : The group where the events are added (i.e. REM).

  2. name : The name of the event (i.e. YASA_REM)

  3. start_sec : The onset of the event in second.

  4. duration_sec : The duration of the event in second.

  5. channels : The list of channels on which the event occurs.

Detector Step

In this step the user can set the parameters for the detection of REMs using YASA. The following parameters can be adjusted:

  • REMs Event group: The group category in the annotation file (default is “REM”).

  • REMs Event name: The name of the event in the annotation file (default is “YASA_REM”).

  • Hypnogram Loaded: If the hypnogram is loaded, the algorithm will use it to filter out REMs.

  • Amplitue: Minimum and maximum amplitude of the peak of the REM.

  • Duration: Minimum and maximum duration of the REM.

  • REM Frequency: Minimum and maximum frequency of the REM.

  • Relative Prominence: Scales the minumum prominence threshold as a fraction of minimum amplitude. (e.g., 0.5 means peaks must stand out by at least half of the minimum amplitude).

  • Sleep Stages: If the hypnogram is loaded, the detection will only be applied to the values defined in “Sleep Stages”: Default = 5 (REM).

  • Remove outliers: If YES, the algorithm will remove outliers detected by the IsolationForest model. Note that this step will only be applied if the number of detected REMs is greater than 50.

Output Report

The output report includes the following characterstics of the REMs:

  • REMs Count

  • Duration of the REMs period in seconds

  • Amplitue of the REMs (Difference between the peak and trough of the [LOC - ROC])

  • Density of the REMs in cycles and hours

  • Variablity of the densities

The report consists of the average of the mentioned characterstics in

  • total (all selected stages)

  • per sleep cycles

  • per clock hour

  • per hour spent in each stage

The tool also provides two folders of REMs characterstics and stages including some parameters for each specific REM event for more detailed analysis.

References

[1] Yetton, B. D., et al. (2016). Automatic detection of rapid eye movements (REMs):A machine learning approach. Journal of neuroscience methods, 259, 72-82.

[2] Agarwal, R., et al. (2005). Detection of rapid-eye movements in sleep studies. IEEE Transactions on biomedical engineering, 52(8), 1390-1396.

Version History

  • v2.0.0Distributed with CEAMS package version 7.0.0 — Snooz beta 2.0.0

    • Initial release of the tool.

  • v3.3.0Distributed with CEAMS package version 7.3.0 - Snooz beta 3.0.0

    • REMs report added to the output of the tool.

    • The UI of the tool has been updated to be more user-friendly.

    • Fixed reporting of events starting at sleep stage transitions.

    • Improve path, filename, and extension handling for sleep cycle warning log file.