Palo, GianpaoloGianpaoloPaloFiorillo, LuigiLuigiFiorilloMonachino, GiulianaGiulianaMonachinoBechny, MichalMichalBechnyWälti, MichelMichelWältiMeier, EliasEliasMeierPentimalli Biscaretti di Ruffia, FrancescaFrancescaPentimalli Biscaretti di RuffiaMelnykowycz, MarkMarkMelnykowyczTzovara, AthinaAthinaTzovara0000-0002-7588-1418Agostini, ValentinaValentinaAgostiniFaraci, Francesca DaliaFrancesca DaliaFaraci2025-01-162025-01-162024https://boris-portal.unibe.ch/handle/20.500.12422/194995Study 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-ear-electroencephalography (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 4-hour signals recorded from 10 healthy subjects aged 18-60 years. Recordings are analyzed following two complementary approaches: (1) a hypnogram-based analysis aimed at assessing the agreement between PSG and in-ear-EEG-derived hypnograms; and (2) a feature- and analysis-based on time- and frequency-domain 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 < .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-nonrapid eye movement, and 0.67 ± 0.10-rapid eye movement-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.enin-ear-EEGmachine learningmultisource-scored sleep databasessleep stagingsleep wearables600 - Technology::610 - Medicine & healtharticle10.48620/846913973573810.1093/sleepadvances/zpae087