Performance of the Dreem 2 EEG headband, relative to polysomnography, for assessing sleep in Parkinson's disease

Abstract

Goal and aims: To pilot the feasibility and evaluate the performance of an EEG wearable for measuring sleep in individuals with Parkinson's disease.

Focus technology: Dreem Headband, Version 2.

Reference technology: Polysomnography.

Sample: Ten individuals with Parkinson's disease.

Design: Individuals wore Dreem Headband during a single night of polysomnography.

Core analytics: Comparison of summary metrics, bias, and epoch-by-epoch analysis.

Additional analytics and exploratory analyses: Correlation of summary metrics with demographic and Parkinson's disease characteristics.

Core outcomes: Summary statistics showed Dreem Headband overestimated several sleep metrics, including total sleep, efficiency, deep sleep, and rapid eye movement sleep, with an exception in light sleep. Epoch-by-epoch analysis showed greater specificity than sensitivity, with adequate accuracy across sleep stages (0.55-0.82).

Important supplemental outcomes: Greater Parkinson's disease duration and rapid eye movement behavior were associated with more wakefulness, and worse Parkinson's disease motor symptoms were associated with less deep sleep.

Core conclusion: The Dreem Headband performs similarly in Parkinson's disease as it did in non-Parkinson's disease samples and shows promise for improving access to sleep assessment in people with Parkinson's disease.

Keywords: Device performance; Parkinson’s disease; Polysomnography (PSG); REM behavior disorder; Sleep; Wearable.