Detect Sleep Spindles

Identifies sleep spindles in EEG data by bandpass filtering in the sigma frequency range, computing an RMS envelope, and detecting continuous segments exceeding a threshold. Spindle frequency is estimated from zero crossings in the bandpassed signal.

Usage

eegSpindleDetect(
  x,
  method = c("sigma", "wavelet"),
  freq_range = c(11, 16),
  min_duration_ms = 500,
  max_duration_ms = 2000,
  threshold_sd = 1.5,
  assay_name = NULL
)

Arguments

x

A PhysioExperiment object with EEG data.

method

Detection method: "sigma" (sigma-band filtering) or "wavelet" (synonym, same algorithm).

freq_range

Numeric vector of length 2 specifying the spindle frequency range in Hz (default: c(11, 16)).

min_duration_ms

Minimum spindle duration in milliseconds (default: 500).

max_duration_ms

Maximum spindle duration in milliseconds (default: 2000).

threshold_sd

Number of standard deviations above the mean for detection threshold (default: 1.5).

assay_name

Input assay name. If NULL, uses the default assay.

Value

A data.frame with columns:

channel

Integer channel index.

start_sample

Integer start sample of the spindle.

end_sample

Integer end sample of the spindle.

duration_ms

Numeric spindle duration in milliseconds.

peak_sample

Integer sample index of peak amplitude.

peak_amplitude

Numeric peak RMS amplitude.

frequency_hz

Numeric estimated spindle frequency in Hz.

References

Berry, R. B., et al. (2017). AASM Scoring Manual Updates for 2017. Journal of Clinical Sleep Medicine, 13(5), 665-666.

Molle, M., et al. (2011). Fast and slow spindles during the sleep slow oscillation. Sleep, 34(10), 1411-1421.

See also

eegSleepStage(), eegKcomplexDetect(), eegSlowWaveDetect(), eegSleepMetrics()

Examples

if (FALSE) { # \dontrun{
pe <- make_eeg_sleep(n_time = 150000, n_channels = 2, sr = 500)
spindles <- eegSpindleDetect(pe)
head(spindles)
} # }