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Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography

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A Correction to this article was published on 17 September 2022

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Abstract

Automatic algorithms are a proposed alternative to manual assessment of polysomnography data for analyzing sleep structure; however, none are acceptably accurate for clinical use. We investigated the feasibility of an automated sleep stage scoring system called Sleep Scope, which is intended for use with portable 1-channel electroencephalograph, and compared it with the traditional polysomnography scoring method. Twenty-six outpatients and fourteen healthy volunteers underwent Sleep Scope and polysomnography assessments simultaneously. Polysomnography records were manually scored by three sleep experts. Sleep Scope records were scored using a dedicated auto-staging algorithm. Sleep parameters, including total sleep time, sleep latency, wake after sleep onset, and sleep efficiency, were calculated. The epoch-by-epoch pairwise concordance based on the classification of sleep into five stages (i.e., wake, rapid eye movement, N1, N2, and N3) was also evaluated after validating homogeneity and bias between Sleep Scope and polysomnography. Compared with polysomnography, Sleep Scope seemed to overestimate sleep latency by approximately 3 min, but there was no consistent tendency in bias in other sleep parameters. The Κ values ranged from 0.66 to 0.75 for experts’ inter-rater polysomnography scores and from 0.62 to 0.67 for Sleep Scope versus polysomnography scores, which indicated sufficient agreement in the determination of sleep stages based on the Landis and Koch criteria. We observed sufficient concordance between Sleep Scope and polysomnography despite lower concordance in sleep disorder patients. Thus, this auto-staging system might serve as a novel clinical tool for reducing the time and expenses required of medical staff and patients.

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Acknowledgements

The authors would like to thank Taeko Toyoda, Hiromi Mastuda, and Tomoko Yamada for their support in implementing the project, as well as Takashi Kanemura and Sachiko Sawada for their support in collecting the portable EEG and PSG recordings. We also thank Dr. Takashi Omori for his advice and support in conducting the statistical analysis. We would also like to thank Editage for English language editing.

Funding

This research was supported in part by the Japan Agency for Medical Research and Development (AMED: https://www.amed.go.jp/en/) under Grant Nos. 16hk0102041h0001, 21uk1024004h0001, and 21uk1024004s0201 and the Japan Society for the Promotion for Science (JSPS: https://www.jsps.go.jp/english/) KAKENHI under Grant Nos. 19K08016, 19H01047, and 16K17332. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Contributions

M.M., H.K., and K.K. designed the study protocol. A.K., M.K., and T.Y. encoded the data, participated in statistical analysis, interpreted the results, and wrote the article under the supervision of K.K. and N.Y. Y.O., Y.K., K.N., and M.T. recruited participants and conducted the study under the management of H.K. M.M. conducted a mathematical analysis. All authors read and approved the final article.

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Correspondence to Kenichi Kuriyama.

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The authors have declared that no competing interests exist.

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This study was approved by the Ethics Committee of Shiga University of Medical Science (Approval No. 29-266).

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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This study was conducted in accordance with the Helsinki Declaration, and written informed consent was obtained from all participants.

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The original online version of this article was revised to update the table 2 to correct the errors.

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41105_2022_421_MOESM4_ESM.png

S1 Fig. Bland–Altman plot analysis of sleep parameters for the patient group. Each plot represents the data for each patient’s sleep parameters. The horizontal solid gray lines represent the means bias (i.e., the differences between the two measures), and the horizontal dotted lines represent the 95% limits of agreement. Positive plots on the vertical axis indicate an overestimation of each sleep parameter by Sleep Scope, and negative plots on the vertical axis indicate an underestimation of each sleep parameter by Sleep Scope, compared to the results from each polysomnography expert. (PNG 102 KB)

41105_2022_421_MOESM5_ESM.png

S2 Fig. Bland–Altman plot analysis of sleep parameters for the healthy group. Each plot represents the data for each healthy participant’s sleep parameters. The horizontal solid gray lines represent the means bias (i.e., the differences between the two measures), and the horizontal dotted lines represent the 95% limits of agreement. Positive plots on the vertical axis indicate an overestimation of each sleep parameter by Sleep Scope, and negative plots on the vertical axis indicate an underestimation of each sleep parameter by Sleep Scope, compared to the results from each polysomnography expert. (PNG 127 KB)

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Kawamura, A., Yoshiike, T., Matsuo, M. et al. Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography. Sleep Biol. Rhythms 21, 85–95 (2023). https://doi.org/10.1007/s41105-022-00421-5

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