Authors:
Ninah Koolen
1
;
Anneleen Dereymaeker
2
;
Katrien Jansen
2
;
Jan Vervisch
2
;
Vladimir Matic
1
;
Maarten De Vos
3
;
Gunnar Naulaers
2
and
Sabine Van Huffel
1
Affiliations:
1
University of Leuven and iMinds-KU Leuven Future Health Department, Belgium
;
2
University Hospital Gasthuisberg, Belgium
;
3
University of Leuven, iMinds-KU Leuven Future Health Department and University of Oldenburg, Belgium
Keyword(s):
Preterm Brain, Symmetry, Channel Symmetry Index, Spectral Power, EEG, One-class SVM, Classification.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Classification
;
Health Engineering and Technology Applications
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Theory and Methods
Abstract:
The automated analysis of the EEG pattern of the preterm newborn would be a valuable tool in the neonatal intensive care units for the prognosis of neurological development. The analysis of the (a)symmetry between the two hemispheres can provide useful information about neuronal dysfunction in early stages. Consecutive and subgroup analyses of different brain regions will allow detecting physiologic asymmetry versus pathologic asymmetry. This can improve the assessment of the long-term neurodevelopmental outcome. We show that pathological asymmetry can be measured and detected using the channel symmetry index, which comprises the difference in power spectral density of contralateral EEG signals. To distinguish pathological from physiological normal EEG patterns, we make use of one-class SVM classifiers.