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@article{Nakamura:2020:10.1109/TBME.2019.2911423,
author = {Nakamura, T and Alqurashi, Y and Morrell, M and Mandic, D},
doi = {10.1109/TBME.2019.2911423},
journal = {IEEE Transactions on Biomedical Engineering},
pages = {203--212},
title = {Hearables: automatic overnight sleep monitoring with standardised in-ear EEG sensor},
url = {http://dx.doi.org/10.1109/TBME.2019.2911423},
volume = {67},
year = {2020}
}
TY - JOUR
AB - 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 scalp-EEG. 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.
AU - Nakamura,T
AU - Alqurashi,Y
AU - Morrell,M
AU - Mandic,D
DO - 10.1109/TBME.2019.2911423
EP - 212
PY - 2020///
SN - 0018-9294
SP - 203
TI - Hearables: automatic overnight sleep monitoring with standardised in-ear EEG sensor
T2 - IEEE Transactions on Biomedical Engineering
UR - http://dx.doi.org/10.1109/TBME.2019.2911423
UR - http://hdl.handle.net/10044/1/70093
VL - 67
ER -