Feasibility of In-Ear Single-Channel ExG for Wearable Sleep~Monitoring in Real-World Settings

2025-09-09

AI Summary

Conventional sleep staging relies on invasive, polysomnographic EEG, limiting long-term monitoring in real-world settings such as home environments. To address this, we propose a novel, unobtrusive wearable paradigm for automatic sleep staging based on single-channel intracranial electrography (ExG) signals acquired from the ear canal. Custom-designed dry-electrode earbuds capture ExG signals, with bilateral ear references employed to suppress common-mode interference; ground-truth labels are concurrently obtained via Apple Watch Ultra. The model is rigorously evaluated using leave-one-subject-out cross-validation. Results demonstrate classification accuracies of 90.5% for binary (awake/sleep) staging and 65.1% for four-class (awake, REM, core NREM, deep NREM) staging. This work constitutes the first systematic validation of intracanal ExG for high-comfort, low-burden, portable, and fully automated sleep staging—establishing a viable pathway toward continuous, real-world sleep monitoring.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

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.

Problem

• Developing unobtrusive in-ear ExG for wearable sleep monitoring
• Validating single-channel in-ear EEG against gold-standard sleep staging
• Enabling real-world applications like automatic media pause at sleep onset

Innovation

• Uses single-channel in-ear ExG signals
• Employs dry eartip electrodes for comfort
• Achieves automatic sleep staging via wearables