Predicting Sleep Classification Performance without Labels

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Predicting Sleep Classification Performance without Labels. / Mikkelsen, Kaare B.; Tabar, Yousef R.; Kidmose, Preben.

42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society. IEEE, 2020. s. 645-648.

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Harvard

Mikkelsen, KB, Tabar, YR & Kidmose, P 2020, Predicting Sleep Classification Performance without Labels. i 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society. IEEE, s. 645-648, 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada, 20/07/2020. https://doi.org/10.1109/EMBC44109.2020.9175743

APA

Mikkelsen, K. B., Tabar, Y. R., & Kidmose, P. (2020). Predicting Sleep Classification Performance without Labels. I 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (s. 645-648). IEEE. https://doi.org/10.1109/EMBC44109.2020.9175743

CBE

Mikkelsen KB, Tabar YR, Kidmose P. 2020. Predicting Sleep Classification Performance without Labels. I 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society. IEEE. s. 645-648. https://doi.org/10.1109/EMBC44109.2020.9175743

MLA

Mikkelsen, Kaare B., Yousef R. Tabar, og Preben Kidmose "Predicting Sleep Classification Performance without Labels". 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society. IEEE. 2020, 645-648. https://doi.org/10.1109/EMBC44109.2020.9175743

Vancouver

Mikkelsen KB, Tabar YR, Kidmose P. Predicting Sleep Classification Performance without Labels. I 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society. IEEE. 2020. s. 645-648 https://doi.org/10.1109/EMBC44109.2020.9175743

Author

Mikkelsen, Kaare B. ; Tabar, Yousef R. ; Kidmose, Preben. / Predicting Sleep Classification Performance without Labels. 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society. IEEE, 2020. s. 645-648

Bibtex

@inproceedings{4c1ef97661b64b0bae495929b0ad1e3d,
title = "Predicting Sleep Classification Performance without Labels",
abstract = "When generating automatic sleep reports with mobile sleep monitoring devices, it is crucial to have a good grasp of the reliability of the result. In this paper, we feed features derived from the output of a sleep scoring algorithm to a 'regression ensemble' to estimate the quality of the automatic sleep scoring. We compare this estimate to the actual quality, calculated using a manual scoring of a concurrent polysomnography recording. We find that it is generally possible to estimate the quality of a sleep scoring, but with some uncertainty ('root mean squared error' between estimated and true Cohen's kappa is 0.078). We expect that this method could be useful in situations with many scored nights from the same subject, where an overall picture of scoring quality is needed, but where uncertainty on single nights is less of an issue.",
author = "Mikkelsen, {Kaare B.} and Tabar, {Yousef R.} and Preben Kidmose",
year = "2020",
doi = "10.1109/EMBC44109.2020.9175743",
language = "English",
pages = "645--648",
booktitle = "42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society",
publisher = "IEEE",
note = "42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",

}

RIS

TY - GEN

T1 - Predicting Sleep Classification Performance without Labels

AU - Mikkelsen, Kaare B.

AU - Tabar, Yousef R.

AU - Kidmose, Preben

PY - 2020

Y1 - 2020

N2 - When generating automatic sleep reports with mobile sleep monitoring devices, it is crucial to have a good grasp of the reliability of the result. In this paper, we feed features derived from the output of a sleep scoring algorithm to a 'regression ensemble' to estimate the quality of the automatic sleep scoring. We compare this estimate to the actual quality, calculated using a manual scoring of a concurrent polysomnography recording. We find that it is generally possible to estimate the quality of a sleep scoring, but with some uncertainty ('root mean squared error' between estimated and true Cohen's kappa is 0.078). We expect that this method could be useful in situations with many scored nights from the same subject, where an overall picture of scoring quality is needed, but where uncertainty on single nights is less of an issue.

AB - When generating automatic sleep reports with mobile sleep monitoring devices, it is crucial to have a good grasp of the reliability of the result. In this paper, we feed features derived from the output of a sleep scoring algorithm to a 'regression ensemble' to estimate the quality of the automatic sleep scoring. We compare this estimate to the actual quality, calculated using a manual scoring of a concurrent polysomnography recording. We find that it is generally possible to estimate the quality of a sleep scoring, but with some uncertainty ('root mean squared error' between estimated and true Cohen's kappa is 0.078). We expect that this method could be useful in situations with many scored nights from the same subject, where an overall picture of scoring quality is needed, but where uncertainty on single nights is less of an issue.

UR - http://www.scopus.com/inward/record.url?scp=85091001327&partnerID=8YFLogxK

U2 - 10.1109/EMBC44109.2020.9175743

DO - 10.1109/EMBC44109.2020.9175743

M3 - Article in proceedings

C2 - 33018070

AN - SCOPUS:85091001327

SP - 645

EP - 648

BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society

PB - IEEE

T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020

Y2 - 20 July 2020 through 24 July 2020

ER -