Automatic Sleep Monitoring Using Ear-EEG

Figures

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  FIGURE 1.. The in-ear sensor used in our study. Left: Wearable in-ear sensor with two flexible electrodes. Right: Placement of the generic earpiece.

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  FIGURE 2.. Comparison with previous studies. A: Evaluation of the agreement between the manually scored hypnograms based on scalp-EEG channels and ear-EEG channels [11]. B: Our analysis framework for establishing the feasibility of ear-EEG in sleep research.

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  FIGURE 3.. Recording setup in our study. Left: The electrodes were placed on scalp and ear. Right: The subject reclined in a comfortable chair.

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  FIGURE 4.. Flowchart for the sleep stage prediction framework adopted in this study (Scenario 2).

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  FIGURE 5.. Spectral edge frequency (SEF) features for the 8 - 15 Hz band. The symbol SEF50 denotes the lowest frequency below which 50% of the total power in a considered frequency band is contained (cf. SEF95 for 95% of total power).

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  FIGURE 6.. Power spectral density for the scalp C3-A2 montage (top) and for the in-ear EEG channel EL1 (bottom).

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  FIGURE 8.. Structural complexity features for different sleep stages. Normalised multi-scale fuzzy entropy (MSFE) from the in-ear EEG channel EL1 is evaluated the over scales 1 (standard FE) to 15, and shows excellent separation between sleep stages. The error bars indicate the standard error.

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  FIGURE 9.. Hypnogram for Subject 2 scored based on scalp-EEG channels (blue) and the automatically predicted label based on in-ear EEG channel EL1 (red) for the 2-class Wake vs Sleep (top) and W-N1 vs N2-N3 (middle) scenarios, and the 4-class (bottom) classification scenario.