Exploratory study of EEG burst characteristics in preterm infants

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.

• Author: Zhayida Simayijiang; Sofia Backman; Johannes Ulén; Sverre Wikström; Karl Åström
• Department/s: 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
• Publishing year: 2013
• Language: English
• Pages: 4295-4298
• Publication/Series: Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
• Full text: - 372 kB; Download statistics
• Links: http://dx.doi.org/10.1109/EMBC.2013.6610495
• Document type: Conference paper
• Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Topic: 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
• Research group: Mathematical Imaging Group
• ISBN/ISSN/Other: ISSN: 1557-170X