Exploratory study of EEG burst characteristics in preterm infants
Författare
Summary, in English
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.
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.
Avdelning/ar
Publiceringsår
2013
Språk
Engelska
Sidor
4295-4298
Publikation/Tidskrift/Serie
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Fulltext
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Dokumenttyp
Konferensbidrag
Förlag
IEEE - Institute of Electrical and Electronics Engineers Inc.
Ämne
- Mathematics
Conference name
35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Conference date
2013-07-03 - 2013-07-07
Conference place
Osaka, Japan
Status
Published
Forskningsgrupp
- Mathematical Imaging Group
ISBN/ISSN/Övrigt
- ISSN: 1557-170X