Feasibility of In-Ear Single-Channel ExG for Wearable Sleep Monitoring in Real-World Settings
This addresses the need for unobtrusive, wearable sleep monitoring for consumers, enabling applications like automatically pausing media, but it is incremental as it builds on prior research showing correlations between in-ear and scalp EEG.
The study tackled the problem of obtrusive sleep monitoring by investigating the feasibility of using single-channel in-ear ExG signals for automatic sleep staging in real-world settings, achieving 90.5% accuracy for binary sleep detection and 65.1% accuracy for four-class staging.
Automatic sleep staging typically relies on gold-standard EEG setups, which are accurate but obtrusive and impractical for everyday use outside sleep laboratories. This limits applicability in real-world settings, such as home environments, where continuous, long-term monitoring is needed. Detecting sleep onset is particularly relevant, enabling consumer applications (e.g. automatically pausing media playback when the user falls asleep). Recent research has shown correlations between in-ear EEG and full-scalp EEG for various phenomena, suggesting wearable, in-ear devices could allow unobtrusive sleep monitoring. We investigated the feasibility of using single-channel in-ear electrophysiological (ExG) signals for automatic sleep staging in a wearable device by conducting a sleep study with 11 participants (mean age: 24), using a custom earpiece with a dry eartip electrode (Dätwyler SoftPulse) as a measurement electrode in one ear and a reference in the other. Ground truth sleep stages were obtained from an Apple Watch Ultra, validated for sleep staging. Our system achieved 90.5% accuracy for binary sleep detection (Awake vs. Asleep) and 65.1% accuracy for four-class staging (Awake, REM, Core, Deep) using leave-one-subject-out validation. These findings demonstrate the potential of in-ear electrodes as a low-effort, comfortable approach to sleep monitoring, with applications such as stopping podcasts when users fall asleep.