Comparison analysis between standard polysomnographic data and in-ear-EEG signals:A preliminary study

Study Objectives: Polysomnography (PSG) currently serves as the benchmark for evaluating sleep disorders. Its discomfort makes long-term monitoring unfeasible, leading to bias in sleep quality assessment. Hence, less invasive, cost-effective, and portable alternatives need to be explored. One promising contender is the in-earElectroencephalography (EEG) sensor. This study aims to establish a methodology to assess the similarity between the single-channel in-ear-EEG and standard PSG derivations. Methods: The study involves four-hour signals recorded from ten healthy subjects aged 18 to 60 years. Recordings are analyzed following two complementary approaches: (i) a hypnogram-based analysis aimed at assessing the agreement between PSG and in-earEEG-derived hypnograms; and (ii) a feature-based analysis based on time- and frequencydomain feature extraction,unsupervised feature selection, and definition of Feature-based Similarity Index via Jensen-Shannon Divergence (JSD-FSI). Results: We find large variability between PSG and in-ear-EEG hypnograms scored by the same sleep expert according to Cohen’s kappa metric, with significantly greater agreements for PSG scorers than for in-ear-EEG scorers (p < 0.001) based on Fleiss’ kappa metric. On average, we demonstrate a high similarity between PSG and in-ear-EEG signals in terms of JSD-FSI - 0.79 ± 0.06 - Awake, 0.77 ± 0.07 - Non-Rapid Eye Movement (NREM), and 0.67 ± 0.10 - Rapid Eye Movement (REM) - and in line with the similarity values computed independently on standard PSG-channel- combinations. Conclusions: In-ear-EEG is a valuable solution for home-based sleep monitoring, however further studies with a larger and more heterogeneous dataset are needed.