Accurate whole-night sleep monitoring with dry-contact ear-EEG

Sleep is a key phenomenon to both understanding, diagnosing and treatment of many illnesses, as well as for studying health and well being in general. Today, the only widely accepted method for clinically monitoring sleep is the polysomnography (PSG), which is, however, both expensive to perform and influences the sleep. This has led to investigations into light weight electroencephalography (EEG) alternatives. However, there has been a substantial performance gap between proposed alternatives and PSG. Here we show results from an extensive study of 80 full night recordings of healthy participants wearing both PSG equipment and ear-EEG. We obtain automatic sleep scoring with an accuracy close to that achieved by manual scoring of scalp EEG (the current gold standard), using only ear-EEG as input, attaining an average Cohen’s kappa of 0.73. In addition, this high performance is present for all 20 subjects. Finally, 19/20 subjects found that the ear-EEG had little to no negative effect on their sleep, and subjects were generally able to apply the equipment without supervision. This finding marks a turning point on the road to clinical long term sleep monitoring: the question should no longer be whether ear-EEG could ever be used for clinical home sleep monitoring, but rather when it will be.

[1]  R. Rosenberg,et al.  The American Academy of Sleep Medicine inter-scorer reliability program: sleep stage scoring. , 2013, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[2]  D. Mandic,et al.  A novel in-ear sensor to determine sleep latency during the Multiple Sleep Latency Test in healthy adults with and without sleep restriction , 2018, Nature and science of sleep.

[3]  Stefan Debener,et al.  Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy , 2018, Journal of sleep research.

[4]  Preben Kidmose,et al.  A Study of Evoked Potentials From Ear-EEG , 2013, IEEE Transactions on Biomedical Engineering.

[5]  P M Zurek,et al.  Detectability of transient and sinusoidal otoacoustic emissions. , 1992, Ear and hearing.

[6]  E. M. Kleinberg,et al.  Stochastic discrimination , 1990, Annals of Mathematics and Artificial Intelligence.

[7]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[8]  Tonya S. King,et al.  Longitudinal Study of Insomnia Symptoms Among Women During Perimenopause , 2017, Journal of obstetric, gynecologic, and neonatal nursing : JOGNN.

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

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

[11]  K. Richards,et al.  Sleep and Long-Term Care. , 2017, Sleep medicine clinics.

[12]  I. Andersen,et al.  Validation of the Danish STOP-Bang obstructive sleep apnoea questionnaire in a public sleep clinic. , 2018, Danish medical journal.

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

[14]  M. Don,et al.  A direct approach for the design of chirp stimuli used for the recording of auditory brainstem responses. , 2010, The Journal of the Acoustical Society of America.

[15]  Farnoush Banaei Kashani,et al.  LIBS: a bioelectrical sensing system from human ears for staging whole-night sleep study , 2018, Commun. ACM.

[16]  Preben Kidmose,et al.  On the Keyhole Hypothesis: High Mutual Information between Ear and Scalp EEG , 2017, Front. Hum. Neurosci..

[17]  Charlene Gamaldo,et al.  The Accuracy, Night-to-Night Variability, and Stability of Frontopolar Sleep Electroencephalography Biomarkers. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[18]  P. Anderer,et al.  Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard , 2009, Journal of sleep research.

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

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

[21]  Michael Labanowski,et al.  Sleep Stage Scoring , 2015 .

[22]  Preben Kidmose,et al.  Automatic sleep staging using ear-EEG , 2017, BioMedical Engineering OnLine.

[23]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[24]  B. Koley,et al.  An ensemble system for automatic sleep stage classification using single channel EEG signal , 2012, Comput. Biol. Medicine.

[25]  Preben Kidmose,et al.  Discrimination of Sleep Spindles in Ear-EEG , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[26]  Zurek Pm,et al.  Detectability of transient and sinusoidal otoacoustic emissions. , 1992 .

[27]  Reza Boostani,et al.  A comparative review on sleep stage classification methods in patients and healthy individuals , 2017, Comput. Methods Programs Biomed..