Evaluation of Dreem headband for sleep staging and EEG spectral analysis in people living with Alzheimer’s and older adults
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Introduction
Portable electroencephalography (EEG) devices offer the potential for accurate quantification of sleep at home but have not been evaluated in relevant populations.
Methods
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]. The accuracy of sleep measures, epoch-by-epoch staging, and the quality of EEG signals for quantitative EEG (qEEG) analysis was 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 (REM) 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 5-stage sleep classification was moderate (MCC=0.54±0.14). After artefact 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 r 2 =0.57; theta r 2 =0.56; alpha r 2 =0.65; sigma power r 2 =0.34.
Conclusion
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.
Statement of Significance
Wearable electroencephalography (EEG) devices such as the Dreem headband (DHB), hold promise for accurate monitoring of sleep at home and improving our understanding of neurodegenerative conditions. This study is important because it provides a comprehensive evaluation of the DHB in older adults, including people living with Alzheimer’s. The DHB automatic sleep staging algorithm demonstrated good concordance with polysomnography for most standard sleep statistics, and epoch-by-epoch sleep/wake classification. A novel aspect of the study is the evaluation of the suitability of the DHB EEG signal for quantitative EEG analysis. Our findings highlight the DHB’s key strengths and provide critical recommendations for improving usability and performance to establish its potential utility for large-scale, objective sleep monitoring in community-dwelling older adults.
