A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning

Alexander Neergaard Olesen; Julie Anja Engelhard Christensen; Helge Bjarup Dissing Sørensen; Poul Jennum

doi:10.1109/EMBC.2016.7591548

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning

Alexander Neergaard Olesen, Julie Anja Engelhard Christensen, Helge Bjarup Dissing Sørensen, Poul Jennum

Copenhagen Center for Health Technology

University of Copenhagen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen’s kappa of 0.74 indicating substantial agreement between automatic and
manual scoring.

Original language	English
Title of host publication	Proceedings of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publisher	IEEE
Publication date	2016
Pages	3769-3772
Article number	ThCT3.14
ISBN (Print)	978-1-4577-0220-4
DOIs	https://doi.org/10.1109/EMBC.2016.7591548
Publication status	Published - 2016
Event	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Orlando, United States Duration: 16 Aug 2016 → 20 Aug 2016 Conference number: 38 http://embc.embs.org/2016/

Conference

Conference	38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Number	38
Country/Territory	United States
City	Orlando
Period	16/08/2016 → 20/08/2016
Internet address	http://embc.embs.org/2016/

Access to Document

10.1109/EMBC.2016.7591548

OpenUrl availability

Full text

Cite this

Olesen, A. N., Christensen, J. A. E., Sørensen, H. B. D., & Jennum, P. (2016). A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning. In Proceedings of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3769-3772). Article ThCT3.14 IEEE. https://doi.org/10.1109/EMBC.2016.7591548

@inproceedings{057483fb6aa24bf3a97e0aa045b96882,

title = "A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning",

abstract = "Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen{\textquoteright}s kappa of 0.74 indicating substantial agreement between automatic andmanual scoring.",

author = "Olesen, {Alexander Neergaard} and Christensen, {Julie Anja Engelhard} and S{\o}rensen, {Helge Bjarup Dissing} and Poul Jennum",

year = "2016",

doi = "10.1109/EMBC.2016.7591548",

language = "English",

isbn = "978-1-4577-0220-4",

pages = "3769--3772",

booktitle = "Proceedings of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",

publisher = "IEEE",

address = "United States",

note = "38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 ; Conference date: 16-08-2016 Through 20-08-2016",

url = "http://embc.embs.org/2016/",

}

Olesen, AN, Christensen, JAE, Sørensen, HBD & Jennum, P 2016, A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning. in Proceedings of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society., ThCT3.14, IEEE, pp. 3769-3772, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, Florida, United States, 16/08/2016. https://doi.org/10.1109/EMBC.2016.7591548

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning. / Olesen, Alexander Neergaard; Christensen, Julie Anja Engelhard; Sørensen, Helge Bjarup Dissing et al.
Proceedings of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2016. p. 3769-3772 ThCT3.14.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning

AU - Olesen, Alexander Neergaard

AU - Christensen, Julie Anja Engelhard

AU - Sørensen, Helge Bjarup Dissing

AU - Jennum, Poul

N1 - Conference code: 38

PY - 2016

Y1 - 2016

N2 - Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen’s kappa of 0.74 indicating substantial agreement between automatic andmanual scoring.

AB - Reducing the number of recording modalities for sleep staging research can benefit both researchers and patients, under the condition that they provide as accurate results as conventional systems. This paper investigates the possibility of exploiting the multisource nature of the electrooculography (EOG) signals by presenting a method for automatic sleep staging using the complete ensemble empirical mode decomposition with adaptive noise algorithm, and a random forest classifier. It achieves a high overall accuracy of 82% and a Cohen’s kappa of 0.74 indicating substantial agreement between automatic andmanual scoring.

U2 - 10.1109/EMBC.2016.7591548

DO - 10.1109/EMBC.2016.7591548

M3 - Article in proceedings

C2 - 28269109

SN - 978-1-4577-0220-4

SP - 3769

EP - 3772

BT - Proceedings of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

PB - IEEE

T2 - 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Y2 - 16 August 2016 through 20 August 2016

ER -

A Noise-Assisted Data Analysis Method for Automatic EOG-Based Sleep Stage Classification Using Ensemble Learning

Abstract

Conference

Access to Document

OpenUrl availability

Fingerprint

Cite this