An examination of sleep spindle metrics in the Sleep Heart Health Study: superiority of automated spindle detection over total sigma power in assessing age-related spindle decline

• DOI: 10.1186/s12883-023-03376-3
• Publication Date: 2023-10-06T07:02:03Z
• Authors: Kalyan Palepu; Kolia Sadeghi; Dave F. Kleinschmidt; Jacob Donoghue; Seth Chapman; Alexander R. Arslan; M. Brandon Westover; Sydney S. Cash; Jay Pathmanathan
• Keywords: Sleep spindle; Sigma

Abstract Background Sleep spindle activity is commonly estimated by measuring sigma power during stage 2 non-rapid eye movement (NREM2) sleep. However, spindles account for little of the total NREM2 interval and therefore over entire may be misleading. This study compares derived measures from direct automated detection with those gross spectral analyses purposes clinical trial design. Methods We in a set 8,440 overnight electroencephalogram (EEG) recordings 5,793 patients Heart Health Study using both detection. Performance two methods was evaluated determining sample size required to detect decline age-related coherence each method simulated trial. Results In trial, 115 achieve 95% identify changes coherence, while only 60. Conclusions Measurements utilizing outperformed conventional analysis wide margin, suggesting that many studies would benefit incorporation These results further suggest some previous which have failed or simply because they were underpowered.