Exploratory study of EEG burst characteristics in preterm infants
Conference Proceeding/Paper
|
Published
|
English
Authors:
Simayijiang, Zhayida
;
Backman, Sofia
;
Ulén, Johannes
;
Wikström, Sverre
;
Åström, Karl
, et al.
Department:
Mathematics (Faculty of Engineering)
Clinical Neurophysiology
Centre for Mathematical Sciences
Mathematical Imaging Group
ELLIIT: the Linköping-Lund initiative on IT and mobile communication
eSSENCE: The e-Science Collaboration
Research Group:
Mathematical Imaging Group
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 results.
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