Detection of K-complexes based on the wavelet transform

Laerke K Krohne; Rikke Rie Hansen; Julie A E Christensen; Helge B D Sorensen; Poul Jennum

doi:10.1109/EMBC.2014.6944859

Detection of K-complexes based on the wavelet transform

Laerke K Krohne, Rikke Rie Hansen, Julie A E Christensen, Helge B D Sorensen, Poul Jennum

Afdeling for Hjerne- og Nerveundersøgelser

13 Citationer (Scopus)

Abstrakt

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.

Originalsprog	Engelsk
Tidsskrift	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
Vol/bind	2014
Sider (fra-til)	5450-3
Antal sider	4
ISSN	2375-7477
DOI	https://doi.org/10.1109/EMBC.2014.6944859
Status	Udgivet - aug. 2014

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10.1109/EMBC.2014.6944859

Citationsformater

Krohne, L. K., Hansen, R. R., Christensen, J. A. E., Sorensen, H. B. D., & Jennum, P. (2014). Detection of K-complexes based on the wavelet transform. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2014, 5450-3. https://doi.org/10.1109/EMBC.2014.6944859

Detection of K-complexes based on the wavelet transform. / Krohne, Laerke K; Hansen, Rikke Rie; Christensen, Julie A E et al.

I: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Bind 2014, 08.2014, s. 5450-3.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

Krohne, LK, Hansen, RR, Christensen, JAE, Sorensen, HBD & Jennum, P 2014, 'Detection of K-complexes based on the wavelet transform', Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, bind 2014, s. 5450-3. https://doi.org/10.1109/EMBC.2014.6944859

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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, {Rikke Rie} and Christensen, {Julie A E} and Sorensen, {Helge B D} and Poul Jennum",

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doi = "10.1109/EMBC.2014.6944859",

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AU - Hansen, Rikke Rie

AU - Christensen, Julie A E

AU - Sorensen, Helge B D

AU - Jennum, Poul

PY - 2014/8

Y1 - 2014/8

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

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VL - 2014

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

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

SN - 2375-7477

ER -

Detection of K-complexes based on the wavelet transform

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