Imperial College London
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ProfessorMaryMorrell

Faculty of Medicine, National Heart & Lung Institute

Professor of Sleep & Respiratory Physiology
 
 
 
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Contact

 

m.morrell

 
 
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Location

 

Room 103ASir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nakamura:2017:10.1109/JTEHM.2017.2702558,
author = {Nakamura, T and Goverdovsky, V and Morrell, M and Mandic, D},
doi = {10.1109/JTEHM.2017.2702558},
journal = {IEEE Journal of Translational Engineering in Health and Medicine},
title = {Automatic sleep monitoring using ear-EEG},
url = {http://dx.doi.org/10.1109/JTEHM.2017.2702558},
volume = {5},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the Electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency (SEF) and multiscale fuzzy entropy (MSFE), a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5% to 95.2% for ear-EEG labels predicted from ear-EEG, and 76.8% to 91.8% for scalp-EEG labels predicted from ear-EEG. The corresponding Kappa coefficients range from 0.64 to 0.83 for Scenario 1, and indicate Substantial to Almost Perfect Agreement, while for Scenario 2 the range of 0.65 to 0.80 indicates Substantial Agreement, thus further supporting the feasibility of in-ear sensing for sleep monitoring in the community.
AU  - Nakamura,T
AU  - Goverdovsky,V
AU  - Morrell,M
AU  - Mandic,D
DO  - 10.1109/JTEHM.2017.2702558
PY  - 2017///
SN  - 2168-2372
TI  - Automatic sleep monitoring using ear-EEG
T2  - IEEE Journal of Translational Engineering in Health and Medicine
UR  - http://dx.doi.org/10.1109/JTEHM.2017.2702558
UR  - http://hdl.handle.net/10044/1/48308
VL  - 5
ER  -
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