Detection of K-complexes based on the wavelet transform

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Detection of K-complexes based on the wavelet transform. / Krohne, Laerke K; Hansen, Rie B; Christensen, Julie A E; Sorensen, Helge B D; Jennum, Poul.

I: I E E E Engineering in Medicine and Biology Society. Conference Proceedings, Bind 2014, 2014, s. 5450-5453.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Krohne, LK, Hansen, RB, Christensen, JAE, Sorensen, HBD & Jennum, P 2014, 'Detection of K-complexes based on the wavelet transform', I E E E Engineering in Medicine and Biology Society. Conference Proceedings, bind 2014, s. 5450-5453. https://doi.org/10.1109/EMBC.2014.6944859

APA

Krohne, L. K., Hansen, R. B., Christensen, J. A. E., Sorensen, H. B. D., & Jennum, P. (2014). Detection of K-complexes based on the wavelet transform. I E E E Engineering in Medicine and Biology Society. Conference Proceedings, 2014, 5450-5453. https://doi.org/10.1109/EMBC.2014.6944859

Vancouver

Krohne LK, Hansen RB, Christensen JAE, Sorensen HBD, Jennum P. Detection of K-complexes based on the wavelet transform. I E E E Engineering in Medicine and Biology Society. Conference Proceedings. 2014;2014:5450-5453. https://doi.org/10.1109/EMBC.2014.6944859

Author

Krohne, Laerke K ; Hansen, Rie B ; Christensen, Julie A E ; Sorensen, Helge B D ; Jennum, Poul. / Detection of K-complexes based on the wavelet transform. I: I E E E Engineering in Medicine and Biology Society. Conference Proceedings. 2014 ; Bind 2014. s. 5450-5453.

Bibtex

@article{962ef941a981426eb103a24fc12432a9,
title = "Detection of K-complexes based on the wavelet transform",
abstract = "Sleep scoring needs computational assistance to reduce execution time and to assure high quality. In this pilot study a semi-automatic K-Complex detection algorithm was developed using wavelet transformation to identify pseudo-K-Complexes and various feature thresholds to reject false positives. The algorithm was trained and tested on sleep EEG from two databases to enhance its general applicability. When testing on data from subjects from the DREAMS{\textcopyright} database, a mean true positive rate of 74 % and a positive predictive value of 65 % were achieved. After adjusting a few thresholds to adapt to the second database, the Danish Center for Sleep Medicine, a similar performance was achieved. The algorithm performs at the level of the State of the Art and surpasses the inter-rater agreement rate.",
author = "Krohne, {Laerke K} and Hansen, {Rie B} and Christensen, {Julie A E} and Sorensen, {Helge B D} and Poul Jennum",
year = "2014",
doi = "10.1109/EMBC.2014.6944859",
language = "English",
volume = "2014",
pages = "5450--5453",
journal = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
issn = "0589-1019",
publisher = "IEEE Signal Processing Society",

}

RIS

TY - JOUR

T1 - Detection of K-complexes based on the wavelet transform

AU - Krohne, Laerke K

AU - Hansen, Rie B

AU - Christensen, Julie A E

AU - Sorensen, Helge B D

AU - Jennum, Poul

PY - 2014

Y1 - 2014

N2 - Sleep scoring needs computational assistance to reduce execution time and to assure high quality. In this pilot study a semi-automatic K-Complex detection algorithm was developed using wavelet transformation to identify pseudo-K-Complexes and various feature thresholds to reject false positives. The algorithm was trained and tested on sleep EEG from two databases to enhance its general applicability. When testing on data from subjects from the DREAMS© database, a mean true positive rate of 74 % and a positive predictive value of 65 % were achieved. After adjusting a few thresholds to adapt to the second database, the Danish Center for Sleep Medicine, a similar performance was achieved. The algorithm performs at the level of the State of the Art and surpasses the inter-rater agreement rate.

AB - Sleep scoring needs computational assistance to reduce execution time and to assure high quality. In this pilot study a semi-automatic K-Complex detection algorithm was developed using wavelet transformation to identify pseudo-K-Complexes and various feature thresholds to reject false positives. The algorithm was trained and tested on sleep EEG from two databases to enhance its general applicability. When testing on data from subjects from the DREAMS© database, a mean true positive rate of 74 % and a positive predictive value of 65 % were achieved. After adjusting a few thresholds to adapt to the second database, the Danish Center for Sleep Medicine, a similar performance was achieved. The algorithm performs at the level of the State of the Art and surpasses the inter-rater agreement rate.

U2 - 10.1109/EMBC.2014.6944859

DO - 10.1109/EMBC.2014.6944859

M3 - Journal article

C2 - 25571227

VL - 2014

SP - 5450

EP - 5453

JO - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

JF - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

SN - 0589-1019

ER -

ID: 137371617