Long-term ear-EEG monitoring of sleep - A case study during shift work

The interest in sleep as a potential clinical biomarker is growing, but the standard method of sleep assessment, polysomnography, is expensive, time consuming, and requires a lot of expert assistance for both set-up and interpretation. To make sleep analysis more available both in research and in the clinic, there is a need for a reliable wearable device for sleep staging. In this case study, we test ear-electroencephalography. A wearable, where electrodes are placed in the outer ear, as a platform for longitudinal at-home recording of sleep. We explore the usability of the ear-electroencephalography in a shift work case with alternating sleep conditions. We find the ear-electroencephalography platform to be reliable both in terms of showing substantial agreement to polysomnography after long-time use (with an overall agreement, using Cohen's kappa, of 0.72) and by being unobtrusive enough to wear during night shift conditions. We find that fractions of non-rapid eye movement sleep and transition probability between sleep stages show great potential as sleep metrics when exploring quantitative differences in sleep architecture between shifting sleep conditions. This study shows that the ear-electroencephalography platform holds great potential as a reliable wearable for quantifying sleep in the wild, pushing this technology further towards clinical adaptation.

Automated summary

The interest in using sleep as a clinical biomarker is increasing, but the standard method of sleep assessment is costly and time-consuming. A reliable wearable device is needed to make sleep analysis more accessible. In this case study, the ear-electroencephalography platform was tested and found to be reliable and unobtrusive for long-term sleep recording. Measurements of non-rapid eye movement sleep duration and transition probability between sleep stages showed promise in quantifying differences in sleep architecture between shifting sleep conditions. This study highlights the potential of the ear-electroencephalography platform for clinical adaptation.