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

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          Abstract

          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.

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          A comparative review on sleep stage classification methods in patients and healthy individuals.

          Proper scoring of sleep stages can give clinical information on diagnosing patients with sleep disorders. Since traditional visual scoring of the entire sleep is highly time-consuming and dependent to experts' experience, automatic schemes based on electroencephalogram (EEG) analysis are broadly developed to solve these problems. This review presents an overview on the most suitable methods in terms of preprocessing, feature extraction, feature selection and classifier adopted to precisely discriminate the sleep stages.
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            The Accuracy, Night-to-Night Variability, and Stability of Frontopolar Sleep Electroencephalography Biomarkers

            Study Objectives: To assess the validity of sleep architecture and sleep continuity biomarkers obtained from a portable, multichannel forehead electroencephalography (EEG) recorder. Methods: Forty-seven subjects simultaneously underwent polysomnography (PSG) while wearing a multichannel frontopolar EEG recording device (Sleep Profiler). The PSG recordings independently staged by 5 registered polysomnographic technologists were compared for agreement with the autoscored sleep EEG before and after expert review. To assess the night-to-night variability and first night bias, 2 nights of self-applied, in-home EEG recordings obtained from a clinical cohort of 63 patients were used (41% with a diagnosis of insomnia/depression, 35% with insomnia/obstructive sleep apnea, and 17.5% with all three). The between-night stability of abnormal sleep biomarkers was determined by comparing each night's data to normative reference values. Results: The mean overall interscorer agreements between the 5 technologists were 75.9%, and the mean kappa score was 0.70. After visual review, the mean kappa score between the autostaging and five raters was 0.67, and staging agreed with a majority of scorers in at least 80% of the epochs for all stages except stage N1. Sleep spindles, autonomic activation, and stage N3 exhibited the least between-night variability ( P < .0001) and strongest between-night stability. Antihypertensive medications were found to have a significant effect on sleep quality biomarkers ( P < .02). Conclusions: A strong agreement was observed between the automated sleep staging and human-scored PSG. One night's recording appeared sufficient to characterize abnormal slow wave sleep, sleep spindle activity, and heart rate variability in patients, but a 2-night average improved the assessment of all other sleep biomarkers. Commentary: Two commentaries on this article appear in this issue on pages 771 and 773. Citation: Levendowski DJ, Ferini-Strambi L, Gamaldo C, Cetel M, Rosenberg R, Westbrook PR. The accuracy, night-to-night variability, and stability of frontopolar sleep electroencephalography biomarkers. J Clin Sleep Med. 2017;13(6):791–803.
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              A direct approach for the design of chirp stimuli used for the recording of auditory brainstem responses.

              A recent study evaluates auditory brainstem responses (ABRs) evoked by chirps of different durations (sweeping rates) [Elberling et al. (2010). J. Acoust. Soc. Am. 128, 215-223]. The study demonstrates that shorter chirps are most efficient at higher levels of stimulation whereas longer chirps are most efficient at lower levels. Mechanisms other than the traveling wave delay, in particular, upward spread of excitation and changes in cochlear-neural delay with level, are suggested to be responsible for these findings. As a consequence, delay models based on estimates of the traveling wave delay are insufficient for the design of chirp stimuli, and another delay model based on a direct approach is therefore proposed. The direct approach uses ABR-latencies from normal-hearing subjects in response to octave-band chirps over a wide range of levels. The octave-band chirps are constructed by decomposing a broad-band chirp, and constitute a subset of the chirp. The delay compensations of the proposed model are similar to those found in the previous experimental study, which thus verifies the results of the proposed model.
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                Author and article information

                Contributors
                mikkelsen.kaare@eng.au.dk
                pki@eng.au.dk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 November 2019
                14 November 2019
                2019
                : 9
                : 16824
                Affiliations
                [1 ]ISNI 0000 0001 1956 2722, GRID grid.7048.b, Department of Engineering, , Aarhus University, ; Aarhus, Denmark
                [2 ]GRID grid.443387.f, Department of Electronic & Telecommunication Engineering, , University of Moratuwa, ; Katubedda, Sri Lanka
                [3 ]UNEEG medical A/S, Lynge, Denmark
                [4 ]ISNI 0000 0004 0512 597X, GRID grid.154185.c, Department of Clinical Neurophysiology, , Aarhus University Hospital, ; Aarhus, Denmark
                Author information
                http://orcid.org/0000-0002-7360-8629
                http://orcid.org/0000-0003-0583-2255
                http://orcid.org/0000-0003-2072-3858
                http://orcid.org/0000-0001-8628-8057
                Article
                53115
                10.1038/s41598-019-53115-3
                6856384
                31727953
                c4ba1cf8-8448-4f05-b0c0-cfbf4b4f9483
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 August 2019
                : 28 October 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/100012774, Innovationsfonden (Innovation Fund Denmark);
                Award ID: 7050-00007
                Award ID: 7050-00007
                Award ID: 7050-00007
                Award Recipient :
                Categories
                Article
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                © The Author(s) 2019

                Uncategorized
                sleep,biomedical engineering
                Uncategorized
                sleep, biomedical engineering

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