Detect Spindles with SUMO

A spindle is “a train of distinct waves with frequency 11-16 Hz (most commonly 12-14 Hz) with a duration ≥0.5 s, usually maximal in amplitude using central derivations” [1]

This tool detects sleep spindles in specific sleep stages using SUMO (Slim U-Net trained on MODA), a deep learning model inspired by the U-Net architecture (Kaulen et al. 2022) [2]. SUMO leverages deep convolutional networks to process minimally preprocessed EEG signals, achieving a higher level of agreement with expert consensus. By automating the spindle identification process with superior precision, SUMO facilitates large-scale spindle studies while reducing the impact of intra- and inter-rater variability observed in manual scoring.

The spindle events detected are added into the accessory file (.tsv, .STS or .ent).

The event information is :

  • group : the group of the event.

  • name : the name of the event.

  • start_sec : the onset of the event in seconds (time elapsed from the lights off).

  • duration_sec : The duration of the event in seconds.

  • channels : The list of channels on which the events occurs.

Two additional output reports are available :

1. Spindle characteristics by event level

Files with the signal characteristic of each spindle event. One file per recording, one row per event.

The characteristics included are :

  • Duration (s)

  • Dominent frequency (Hz), where spectral energy is maximum

  • Average frequency (Hz) counting peaks

  • Peak-to-peak amplitude (µV)

  • Root Mean Square (rms) amplitude (µV)

2. Spindle characteristics averaged by subject level

A file with the spindle characteristics averaged per subject. One file for the cohort, one row per channel.

The characteristics included are :

  • spindle count

  • the average spindle characteristics listed above

    • total (all selected stages)

    • per sleep stage

    • per sleep cycle

    • per clock hour

    • per hour spent in each sleep stage

See Spindle cohort report (SUMO) for the variable definition.

Filtering Information

Before spindle detection, the EEG signal is band-pass filtered to 0.3-30 Hz (10th order, but halved before the forward/backward pass) and downsampled to 100 Hz as preprocessing steps. The filter is a Butterworth designs implemented in second-order sections (SOS) and applied with bidirectional zero-phase filtering. This approach preserves the desired magnitude response while eliminating phase distortion.

Bandpass filter parameters:

  • Type: IIR bandpass

  • Family: Butterworth

  • Frequency band: 0.3-30 Hz

  • Order: 10 (internally halved before the forward/backward pass)

  • Form: second-order sections (SOS)

  • Application: bidirectional zero-phase filtering (filtfilt)

Steps

Common settings
Define the sleep cycles criteria for your study.
For more information, see Definition of Sleep Cycles.

1 - Input Files

Start by opening your PSG files (.edf, .sts or .eeg).

  • European Data Format (EDF) :

    The corresponding .tsv file is required with .edf. 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.

2 - Non valid events

Select events to disable the spindle detection.

Note

If artefacts have not already been identified and saved in the accessory file, it is recommended to use the Detect EEG Artifacts tool in the Preprocessing module before running SUMO for better detection accuracy.

3 - Detection Settings

The user must define the following parameters:
Spindle Duration: Define the minimum and maximum duration of retained spindles.
Sleep Stage: Choose the sleep stage(s) in which to detect spindles.
Sleep Cycle: Optionally detect spindles only within sleep cycles.
REM Period: Optionally exclude REM periods from the analysis.

4 - Output Files

Select which reports to generate.

Report

References

[1] Iber, C., American Academy of Sleep Medicine, 2007. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications. American Academy of Sleep Medicine.

[2] Kaulen, L., Schwabedal, J.T., Schneider, J., Ritter, P. and Bialonski, S., 2022. Advanced sleep spindle identification with neural networks. Scientific reports, 12(1), p.7686. https://doi.org/10.1038/s41598-022-11210-y

Version History

The version history of this tool is as follows:

  • v2.0.1Distributed with CEAMS package version 7.2.0 - Snooz beta 2.0.1

    • Initial release of the tool.

  • v2.8.0Distributed with CEAMS package version 7.3.0 - Snooz beta 3.0.0

    • N2 and N3 are selected by default to run the spindle detection.

    • Refactored the output report to distinguish between elapsed clock time and sleep-stage time.

    • Added new variables representing the combined N2 + N3 stages.

    • Events are discarded during non-specific channel artifacts.

    • Fixed reporting of events starting at sleep stage transitions.

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