Sleep spindles comprise a subset of a broader class of electroencephalogram events.

STUDY OBJECTIVES: Sleep spindles are defined based on expert observations of waveform features in the electroencephalogram traces. This is a potentially limiting characterization, as transient oscillatory bursts like spindles are easily obscured in the time-domain by higher amplitude activity at other frequencies or by noise. It is therefore highly plausible that many relevant events are missed by current approaches based on traditionally-defined spindles. Given their oscillatory structure, we reexamine spindle activity from first principles, using time-frequency activity in comparison to scored spindles.

METHODS: Using multitaper spectral analysis, we observe clear time-frequency peaks in the sigma (10-16 Hz) range (TFσ peaks). While nearly every scored spindle coincides with a TFσ peak, numerous similar TFσ peaks remain undetected. We therefore perform statistical analyses of spindles and TFσ peaks using manual and automated detection methods, comparing event co-occurrence, morphological similarities, and night-to-night consistency across multiple datasets.

RESULTS: On average, TFσ peaks have more than 3 times the rate of spindles (mean rate: 9.8 vs. 3.1 events/min). Moreover, spindles subsample the most prominent TFσ peaks with otherwise identical spectral morphology. We further demonstrate that detected TFσ peaks have stronger night-to-night rate stability (rho = 0.98) than spindles (rho = 0.67), while covarying with spindle rates across subjects (rho = 0.72).

CONCLUSIONS: These results provide compelling evidence that traditionally-defined spindles constitute a subset of a more generalized class of electroencephalogram events. TFσ peaks are therefore a more complete representation of the underlying phenomenon, providing a more consistent and robust basis for future experiments and analyses.