Exploratory study of EEG burst characteristics in preterm infants
Simayijiang, Zhayida LU ; Backman, Sofia LU ; Ulén, Johannes LU ; Wikström, Sverre and Åström, Karl LU
(2013)
35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
p.4295-4298
- Abstract
- In this paper, we study machine learning techniques
and features of electroencephalography activity bursts
for predicting outcome in extremely preterm infants. It was
previously shown that the distribution of interburst interval
durations predicts clinical outcome, but in previous work the
information within the bursts has been neglected. In this paper,
we perform exploratory analysis of feature extraction of burst
characteristics and use machine learning techniques to show
that such features could be used for outcome prediction. The
results are promising, but further verification of the results in
larger datasets is needed to obtain conclusive... (More) - In this paper, we study machine learning techniques
and features of electroencephalography activity bursts
for predicting outcome in extremely preterm infants. It was
previously shown that the distribution of interburst interval
durations predicts clinical outcome, but in previous work the
information within the bursts has been neglected. In this paper,
we perform exploratory analysis of feature extraction of burst
characteristics and use machine learning techniques to show
that such features could be used for outcome prediction. The
results are promising, but further verification of the results in
larger datasets is needed to obtain conclusive results. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4249682
- author
- Simayijiang, Zhayida
LU
; Backman, Sofia
LU
; Ulén, Johannes
LU
; Wikström, Sverre
and Åström, Karl
LU
- organization
- publishing date
- 2013
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- conference location
- Osaka, Japan
- conference dates
- 2013-07-03 - 2013-07-07
- external identifiers
-
- wos:000341702104184
- pmid:24110682
- scopus:84886473427
- ISSN
- 1557-170X
- DOI
- 10.1109/EMBC.2013.6610495
- language
- English
- LU publication?
- yes
- id
- 098c5c71-ff2f-4446-a12a-1aef8d016828 (old id 4249682)
- date added to LUP
- 2016-04-01 13:33:25
- date last changed
- 2022-01-27 19:47:33
@inproceedings{098c5c71-ff2f-4446-a12a-1aef8d016828,
abstract = {{In this paper, we study machine learning techniques<br/><br>
and features of electroencephalography activity bursts<br/><br>
for predicting outcome in extremely preterm infants. It was<br/><br>
previously shown that the distribution of interburst interval<br/><br>
durations predicts clinical outcome, but in previous work the<br/><br>
information within the bursts has been neglected. In this paper,<br/><br>
we perform exploratory analysis of feature extraction of burst<br/><br>
characteristics and use machine learning techniques to show<br/><br>
that such features could be used for outcome prediction. The<br/><br>
results are promising, but further verification of the results in<br/><br>
larger datasets is needed to obtain conclusive results.}},
author = {{Simayijiang, Zhayida and Backman, Sofia and Ulén, Johannes and Wikström, Sverre and Åström, Karl}},
booktitle = {{Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE}},
issn = {{1557-170X}},
language = {{eng}},
pages = {{4295--4298}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Exploratory study of EEG burst characteristics in preterm infants}},
url = {{https://lup.lub.lu.se/search/files/3445664/4249684.pdf}},
doi = {{10.1109/EMBC.2013.6610495}},
year = {{2013}},
}