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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:
author
organization
publishing date
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
  • 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
2014-02-12 15:59:29
date last changed
2019-02-20 05:40:15
@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},
  issn         = {1557-170X},
  language     = {eng},
  location     = {Osaka, Japan},
  pages        = {4295--4298},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Exploratory study of EEG burst characteristics in preterm infants},
  url          = {http://dx.doi.org/10.1109/EMBC.2013.6610495},
  year         = {2013},
}