TY - JOUR
AU1 - G Ravindran, Kiran K
AU2 - della Monica, Ciro
AU3 - Atzori, Giuseppe
AU4 - Nilforooshan, Ramin
AU5 - Hassanin, Hana
AU6 - Revell, Victoria
AU7 - Dijk, Derk-Jan
AU8 - 
AB - <jats:title>Abstract</jats:title>
               <jats:sec>
                  <jats:title>Study Objectives</jats:title>
                  <jats:p>Portable electroencephalography (EEG) devices offer the potential for accurate quantification of sleep at home but have not been evaluated in relevant populations. We assessed the Dreem headband (DHB) and its automated sleep staging algorithm in 62 older adults [age (mean±SD) 70.5±6.7 years; 12 Alzheimer’s].</jats:p>
               </jats:sec>
               <jats:sec>
                  <jats:title>Methods</jats:title>
                  <jats:p>The accuracy of sleep measures, epoch-by-epoch staging, and the quality of EEG signals for quantitative EEG (qEEG) analysis were compared to standard polysomnography (PSG) in a sleep laboratory.</jats:p>
               </jats:sec>
               <jats:sec>
                  <jats:title>Results</jats:title>
                  <jats:p>The DHB algorithm accurately estimated total sleep time (TST) and sleep efficiency (SEFF) with a Symmetric Mean Absolute Percentage Error (SMAPE) &amp;lt;10%. Wake after sleep onset (WASO) and number of awakenings (NAW) were underestimated (WASO: ~17 minutes; NAW: ~9 counts) with SMAPE &amp;lt;20%. Sleep onset latency (SOL) was overestimated by ~30 minutes when using the entire DHB recording period, but it was accurate (Bias: 0.3 minutes) when estimated over the lights-off period. Stage N3 and total non-rapid eye movement (NREM) sleep durations were estimated accurately (Bias &amp;lt;20 minutes), while REM sleep was overestimated (~25 minutes; SMAPE: ~24%). Epoch-by-epoch sleep/wake classification showed acceptable performance (MCC=0.77±0.17) and 5-stage sleep classification was moderate (MCC=0.54±0.14). After artifact removal, 73% of the recordings were usable for qEEG analysis. Concordance (p&amp;lt;0.001) of EEG band power ranged from moderate to good: slow wave activity r2=0.57; theta r2=0.56; alpha r2=0.65; sigma power r2=0.34.</jats:p>
               </jats:sec>
               <jats:sec>
                  <jats:title>Conclusions</jats:title>
                  <jats:p>DHB algorithm provides accurate estimates of several sleep measures and qEEG metrics. However, further improvement in REM detection is needed to enhance its utility for research and clinical applications.</jats:p>
               </jats:sec>
TI - Evaluation of Dreem headband for sleep staging and EEG spectral analysis in people living with Alzheimer’s and older adults
JF - SLEEP
DO - 10.1093/sleep/zsaf122
DA - 2025-05-04
UR - https://www.deepdyve.com/lp/crossref/evaluation-of-dreem-headband-for-sleep-staging-and-eeg-spectral-jdeCd8GD2x
DP - DeepDyve
ER -