Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity

2.50
Hdl Handle:
http://hdl.handle.net/10547/279174
Title:
Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity
Authors:
Jakaite, Livija; Schetinin, Vitaly ( 0000-0003-1826-0153 ) ; Schult, Joachim
Abstract:
We explored the feature extraction techniques for Bayesian assessment of EEG maturity of newborns in the context that the continuity of EEG is the most important feature for assessment of the brain development. The continuity is associated with EEG “stationarity” which we propose to evaluate with adaptive segmentation of EEG into pseudo-stationary intervals. The histograms of these intervals are then used as new features for the assessment of EEG maturity. In our experiments, we used Bayesian model averaging over decision trees to differentiate two age groups, each included 110 EEG recordings. The use of the proposed EEG features has shown, on average, a 6% increase in the accuracy of age differentiation.
Citation:
Jakaite, L.; Schetinin, V.; Schult, J., (2011) 'Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity,' Computer-Based Medical Systems (CBMS), 2011 24th International Symposium on: 1-6
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date:
2011
URI:
http://hdl.handle.net/10547/279174
DOI:
10.1109/CBMS.2011.5999109
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5999109
Type:
Conference papers, meetings and proceedings
Language:
en
ISBN:
9781457711893
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorJakaite, Livijaen_GB
dc.contributor.authorSchetinin, Vitalyen_GB
dc.contributor.authorSchult, Joachimen_GB
dc.date.accessioned2013-04-07T16:29:42Z-
dc.date.available2013-04-07T16:29:42Z-
dc.date.issued2011-
dc.identifier.citationJakaite, L.; Schetinin, V.; Schult, J., (2011) 'Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity,' Computer-Based Medical Systems (CBMS), 2011 24th International Symposium on: 1-6en_GB
dc.identifier.isbn9781457711893-
dc.identifier.doi10.1109/CBMS.2011.5999109-
dc.identifier.urihttp://hdl.handle.net/10547/279174-
dc.description.abstractWe explored the feature extraction techniques for Bayesian assessment of EEG maturity of newborns in the context that the continuity of EEG is the most important feature for assessment of the brain development. The continuity is associated with EEG “stationarity” which we propose to evaluate with adaptive segmentation of EEG into pseudo-stationary intervals. The histograms of these intervals are then used as new features for the assessment of EEG maturity. In our experiments, we used Bayesian model averaging over decision trees to differentiate two age groups, each included 110 EEG recordings. The use of the proposed EEG features has shown, on average, a 6% increase in the accuracy of age differentiation.en_GB
dc.language.isoenen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_GB
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5999109en_GB
dc.subjectBayesian methodsen_GB
dc.subjectbrain modelingen_GB
dc.titleFeature extraction from electroencephalograms for Bayesian assessment of newborn brain maturityen
dc.typeConference papers, meetings and proceedingsen
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