At-home sleep monitoring using generic ear-EEG

Introduction A device comprising two generic earpieces with embedded dry electrodes for ear-centered electroencephalography (ear-EEG) was developed. The objective was to provide ear-EEG based sleep monitoring to a wide range of the population without tailoring the device to the individual. Methods To validate the device ten healthy subjects were recruited for a 12-night sleep study. The study was divided into two parts; part A comprised two nights with both ear-EEG and polysomnography (PSG), and part B comprised 10 nights using only ear-EEG. In addition to the electrophysiological measurements, subjects filled out a questionnaire after each night of sleep. Results The subjects reported that the ear-EEG system was easy to use, and that the comfort was better in part B. The performance of the system was validated by comparing automatic sleep scoring based on ear-EEG with PSG-based sleep scoring performed by a professional trained sleep scorer. Cohen’s kappa was used to assess the agreement between the manual and automatic sleep scorings, and the study showed an average kappa value of 0.71. The majority of the 20 recordings from part A yielded a kappa value above 0.7. The study was compared to a companioned study conducted with individualized earpieces. To compare the sleep across the two studies and two parts, 7 different sleeps metrics were calculated based on the automatic sleep scorings. The ear-EEG nights were validated through linear mixed model analysis in which the effects of equipment (individualized vs. generic earpieces), part (PSG and ear-EEG vs. only ear-EEG) and subject were investigated. We found that the subject effect was significant for all computed sleep metrics. Furthermore, the equipment did not show any statistical significant effect on any of the sleep metrics. Discussion These results corroborate that generic ear-EEG is a promising alternative to the gold standard PSG for sleep stage monitoring. This will allow sleep stage monitoring to be performed in a less obtrusive way and over longer periods of time, thereby enabling diagnosis and treatment of diseases with associated sleep disorders.

[1]  Kaare B. Mikkelsen,et al.  Self-applied ear-EEG for sleep monitoring at home , 2022, 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[2]  Kaare B. Mikkelsen,et al.  Sleep Monitoring Using Ear-Centered Setups: Investigating the Influence From Electrode Configurations , 2021, IEEE Transactions on Biomedical Engineering.

[3]  Kaare B. Mikkelsen,et al.  EEGs Vary Less Between Lab and Home Locations Than They Do Between People , 2021, Frontiers in Computational Neuroscience.

[4]  Kaare B. Mikkelsen,et al.  Ear-EEG for sleep assessment: a comparison with actigraphy and PSG , 2020, Sleep and Breathing.

[5]  Kaare B. Mikkelsen,et al.  Muscle Activity Detection during Sleep by Ear-EEG , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[6]  Kaare B. Mikkelsen,et al.  Predicting Sleep Classification Performance without Labels , 2020, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[7]  K. Melehan,et al.  An Australasian Commentary on the AASM Manual for the Scoring of Sleep and Associated Events , 2020, Sleep and Biological Rhythms.

[8]  Danilo Mandic,et al.  Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor , 2020, IEEE Transactions on Biomedical Engineering.

[9]  Preben Kidmose,et al.  Accurate whole-night sleep monitoring with dry-contact ear-EEG , 2019, Scientific Reports.

[10]  B. Högl,et al.  Sleep in Parkinson’s disease , 2019, Neuropsychopharmacology.

[11]  Hugo R. Jourde,et al.  The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging , 2019, bioRxiv.

[12]  Laura Verga,et al.  The risk of neurodegeneration in REM sleep behavior disorder: A systematic review and meta-analysis of longitudinal studies. , 2019, Sleep medicine reviews.

[13]  Preben Kidmose,et al.  Dry-Contact Electrode Ear-EEG , 2019, IEEE Transactions on Biomedical Engineering.

[14]  Heecheon You,et al.  Anthropometric analysis of 3D ear scans of Koreans and Caucasians for ear product design , 2018, Ergonomics.

[15]  Bingqian Zhu,et al.  Sleep assessment in aging adults with type 2 diabetes: agreement between actigraphy and sleep diaries. , 2018, Sleep medicine.

[16]  F. Hölzle,et al.  Three-Dimensional Analysis of the Ear Morphology , 2018, Aesthetic Plastic Surgery.

[17]  Vasant Honavar,et al.  Toward personalized sleep-wake prediction from actigraphy , 2018, 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[18]  Kaare B. Mikkelsen,et al.  Automatic sleep staging using ear-EEG , 2017, Biomedical engineering online.

[19]  S. Debener,et al.  Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG , 2017, Front. Hum. Neurosci..

[20]  Eleni Giannouli,et al.  Performance of a New Portable Wireless Sleep Monitor. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[21]  Qiang Li,et al.  A wearable ear-EEG recording system based on dry-contact active electrodes , 2016, 2016 IEEE Symposium on VLSI Circuits (VLSI-Circuits).

[22]  Kaare B. Mikkelsen,et al.  EEG Recorded from the Ear: Characterizing the Ear-EEG Method , 2015, Front. Neurosci..

[23]  A. Sadeh.  Iii. Sleep assessment methods. , 2015, Monographs of the Society for Research in Child Development.

[24]  K. Rosenkranz,et al.  An in-the-ear platform for recording electroencephalogram , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  S. Kagamimori,et al.  Validity of Child Sleep Diary Questionnaire among Junior High School Children , 2005, Journal of epidemiology.

[26]  Rauno Pääkkönen,et al.  Noise attenuation and proper insertion of earplugs into ear canals. , 2002, The Annals of occupational hygiene.

[27]  C. E. Rogers,et al.  Symbolic Description of Factorial Models for Analysis of Variance , 1973 .

[28]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[29]  Preben Kidmose,et al.  In-Ear EEG From Viscoelastic Generic Earpieces: Robust and Unobtrusive 24/7 Monitoring , 2016, IEEE Sensors Journal.

[30]  N. Rogers,et al.  Sleep-wake changes and cognition in neurodegenerative disease. , 2011, Progress in brain research.

[31]  A. Chesson,et al.  The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications , 2007 .

[32]  W. B. Webb,et al.  The first night effect: an EEG study of sleep. , 1966, Psychophysiology.