Evaluation of Dreem headband for sleep staging and EEG spectral analysis in people living with Alzheimer’s disease and older adults
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Study objectives
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 disease].
Methods
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.
Results
The DHB algorithm accurately estimated total sleep time (TST) and sleep efficiency (SEFF) with a symmetric mean absolute percentage error (SMAPE) < 10%. Wake after sleep onset (WASO) and number of awakenings (NAW) were underestimated (WASO: ~17 minutes; NAW: ~9 counts) with SMAPE < 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 < 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 five-stage sleep classification was moderate (MCC = 0.54 ± 0.14). After artifact removal, 73% of the recordings were usable for qEEG analysis. Concordance (p < 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.
Conclusions
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.
