Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor

Abstract

Objective: Advances in sensor miniaturisation and computational power have served as enabling technologies for monitoring human physiological conditions in real-world scenarios. Sleep disruption may impact neural function, and can be a symptom of both physical and mental disorders. This study proposes wearable in-ear electroencephalography (ear-EEG) for overnight sleep monitoring as a 24/7 continuous and unobtrusive technology for sleep quality assessment in the community. Methods: Twenty-two healthy participants took part in overnight sleep monitoring with simultaneous ear-EEG and conventional full polysomnography (PSG) recordings. The ear-EEG data were analysed in the both structural complexity and spectral domains; the extracted features were used for automatic sleep stage prediction through supervised machine learning, whereby the PSG data were manually scored by a sleep clinician. Results: The agreement between automatic sleep stage prediction based on ear-EEG from a single in-ear sensor and the hypnogram based on the full PSG was 74.1 % in the accuracy over five sleep stage classification; this is supported by a Substantial Agreement in the kappa metric (0.61). Conclusion: The in-ear sensor is both feasible for monitoring overnight sleep outside the sleep laboratory and mitigates technical difficulties associated with PSG. It therefore represents a 24/7 continuously wearable alternative to conventional cumbersome and expensive sleep monitoring. Significance: The 'standardised' one-size-fits-all viscoelastic in-ear sensor is a next generation solution to monitor sleep-this technology promises to be a viable method for readily wearable sleep monitoring in the community, a key to affordable healthcare and future eHealth.

Save Muscle Information–Unfiltered EEG Signal Helps Distinguish Sleep Stages

“…There are several possible applications of recycling the muscle tone information from the EEG signal. Recently, an increasing number of wearable biosignal amplifiers have been developed (e.g., around ear applications, wearable headbands) to obtain large-scale data and allow for in-field, long-term assessments of sleep [7][8][9]. One important aspect of these wearable devices is a non-obtrusive design and therefore a reduction of electrodes is desired.…”

“…Furthermore, the raw data might be dumped from the commercial polysomnogram (PSG) and the raw data might have been filtered at the frequency range 0.3-35 Hz. Due to the advance of technology and focus on large-scale applications, there is an increasing number of mobile devices capable of recording EEG signal in in-home settings (e.g., single channel amplifiers on the forehead or in-ear/around ear applications) [6][7][8][9]. These mobile solutions might not include EMG and EOG information, and the raw data might be bandpassed for different purposes, like data storage or transmission.…”

In-Ear SpO2: A Tool for Wearable, Unobtrusive Monitoring of Core Blood Oxygen Saturation

“…vital signs and neural function, as the head does not move much in daily life unlike sites such as the wrist. Owing to these desirable properties, the ear-canal has been established as a feasible wearable site by Ear-EEG [ 15 , 16 ] and Ear-ECG [ 14 ].…”

The future of sleep health: a data-driven revolution in sleep science and medicine

“…In ear, EEG is a modality that has shown promise in recent years, for instance, Mikkelsen et al 62 compared in-ear mobile EEG analysed through machine learning-based automated scoring to conventional, manual scored PSG and commercial-grade actigraphy showed promising results, although also constrained to a laboratory environment. A 2019 study showed that automatic sleep stage prediction based on a single in-ear sensor demonstrated a 74% agreement with the hypnogram generated from full PSG, which is promising but still requires further work for it to be at a clinical standard of performance (90% agreement) 63 . These devices are particularly interesting given their potential for freeliving application.…”