Comparison analysis between standard polysomnographic data and in-ear-electroencephalography signals: a preliminary study.
Gianpaolo Palo, Luigi Fiorillo, Giuliana Monachino, Michal Bechny, Michel W��lti, Elias Meier, Francesca Pentimalli Biscaretti di Ruffia, Mark Melnykowycz, Athina Tzovara, Valentina Agostini, Francesca Dalia Faraci
Author Information
Gianpaolo Palo: Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
Luigi Fiorillo: Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland. ORCID
Giuliana Monachino: Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
Michal Bechny: Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
Michel W��lti: IDUN Technologies AG, Glattpark, Switzerland.
Elias Meier: IDUN Technologies AG, Glattpark, Switzerland.
Francesca Pentimalli Biscaretti di Ruffia: IDUN Technologies AG, Glattpark, Switzerland.
Mark Melnykowycz: IDUN Technologies AG, Glattpark, Switzerland.
Athina Tzovara: Institute of Computer Science, University of Bern, Bern, Switzerland.
Valentina Agostini: Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy. ORCID
Francesca Dalia Faraci: Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare (MeDiTech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
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-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 ( < .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.
