Automatic sleep monitoring using ear-EEG

TL;DR

This study demonstrates that an in-ear EEG sensor can accurately classify sleep stages, offering a comfortable and unobtrusive method for long-term sleep monitoring suitable for community use.

Contribution

The paper introduces a novel in-ear EEG system combined with spectral and complexity features for automatic sleep stage classification, showing promising accuracy and agreement with scalp EEG.

Findings

Achieved up to 95.2% accuracy in classifying ear-EEG sleep stages.

Kappa coefficients indicate substantial to almost perfect agreement with scalp EEG.

Feasibility of unobtrusive in-ear sleep monitoring demonstrated.

Abstract

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 multi- scale 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, which range from 0.64 to 0.83 for Scenario 1 and from 0.65 to 0.80 for Scenario 2, indicate a Substantial to Almost Perfect agreement, thus proving the feasibility of in-ear sensing for sleep monitoring in the community.