Detect sleep spindles — compute

Detects sleep spindles in EEG channels using a band-pass / RMS envelope approach, closely following the algorithm in Lacourse et al. (2019) and implemented in YASA (Vallat & Walker, 2021). Spindles are identified as transient bursts of 11–16 Hz activity during NREM sleep.

Usage

compute_spindles(
  psg,
  channel = NULL,
  stages = NULL,
  freq_spindle = c(11, 16),
  rms_window_s = 0.3,
  min_duration_s = 0.5,
  max_duration_s = 3,
  threshold_sd = 1.5
)

Arguments

psg: An mrpheus_psg object from prepare_psg().
channel: Character. EEG channel label. If NULL (default), the first non-bad EEG channel is used.
stages: Integer vector or NULL. Epoch indices (1-based) restricted to NREM sleep. If NULL, spindles are detected across all epochs.
freq_spindle: Numeric vector of length 2. Spindle frequency band (Hz). Default c(11, 16).
rms_window_s: Numeric. Moving RMS window length in seconds. Default 0.3.
min_duration_s: Numeric. Minimum spindle duration in seconds. Default 0.5.
max_duration_s: Numeric. Maximum spindle duration in seconds. Default 3.0.
threshold_sd: Numeric. RMS threshold as a multiple of the channel SD (within sigma band). Default 1.5.

Value

A tibble with one row per detected spindle:

epoch: Integer. Epoch in which the spindle was detected.
start_s: Numeric. Spindle onset relative to epoch start (seconds).
end_s: Numeric. Spindle offset relative to epoch start (seconds).
duration_s: Numeric. Spindle duration in seconds.
peak_freq_hz: Numeric. Peak frequency within the spindle.
rms_uv: Numeric. Mean RMS amplitude within the spindle (µV).
channel: Character. Channel label.

References

Lacourse, K., Yetton, B., Mednick, S., & Bhatt, D. L. (2019). Massive online sleep staging: A polysomnography data repository. Journal of Sleep Research.

Vallat, R., & Walker, M. P. (2021). An open-source, high-performance tool for automated sleep staging. eLife, 10, e70092. doi:10.7554/eLife.70092