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

BMC Neurology

Research

Kalyan Palepu¹  Kolia Sadeghi¹  Seth Chapman¹  Alexander R. Arslan¹  Dave F. Kleinschmidt¹  Jay Pathmanathan¹  Jacob Donoghue¹  M. Brandon Westover²  Sydney S. Cash³ 

[1] Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, 02210, Boston, MA, USA;Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, 02210, Boston, MA, USA;Beth Israel Deaconess Medical Center, 330 Brookline Ave, 02215, Boston, MA, USA;Beacon Biosignals, 22 Boston Wharf Rd 7th Floor, Suite 41, 02210, Boston, MA, USA;Clinical Data Animation Center (CDAC), Massachusetts General Hospital, 50 Staniford Street, Fruit St, 02114, Boston, MA, USA;

关键词: Sleep Spindles;    Sigma Power;    Sigma Coherence;    EEG;    Spindle coherence;    Drug development;   

DOI  :  10.1186/s12883-023-03376-3

 received in 2022-07-06, accepted in 2023-09-08,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundSleep 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 sigma power over the entire interval may be misleading. This study compares derived spindle measures from direct automated spindle detection with those from gross power spectral analyses for the purposes of clinical trial design.MethodsWe estimated spindle activity in a set of 8,440 overnight electroencephalogram (EEG) recordings from 5,793 patients from the Sleep Heart Health Study using both sigma power and direct automated spindle detection. Performance of the two methods was evaluated by determining the sample size required to detect decline in age-related spindle coherence with each method in a simulated clinical trial.ResultsIn a simulated clinical trial, sigma power required a sample size of 115 to achieve 95% power to identify age-related changes in sigma coherence, while automated spindle detection required a sample size of only 60.ConclusionsMeasurements of spindle activity utilizing automated spindle detection outperformed conventional sigma power analysis by a wide margin, suggesting that many studies would benefit from incorporation of automated spindle detection. These results further suggest that some previous studies which have failed to detect changes in sigma power or coherence may have failed simply because they were underpowered.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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