Automatic classification of background EEG activity in healthy and sick neonates
Journal article, 2010
The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were displaying a burst suppression pattern while suffering from the
after-effects of asphyxia during birth was included along with EEG from 20 full-term healthy newborn babies. The signals from the healthy babies were divided into four behavioural states: active awake, quiet awake, active sleep and quiet sleep. By using a number of features
extracted from the EEG together with Fisher’s linear discriminant classifier we have managed to achieve 100% correct classification when separating burst suppression EEG from all four healthy EEG types and 93% true positive classification when separating quiet sleep from the
other types. The other three sleep stages could not be classified. When the pathological burst suppression pattern was detected, the analysis was taken one step further and the signal was segmented into burst and suppression, allowing clinically relevant parameters such as suppression length and burst suppression ratio to be calculated. The segmentation of the burst suppression EEG works well, with a probability of error around 4%.
EEG
Physiologic
Infant
Linear Models
physiopathology
Brain
physiology
Humans
signal processing
Wakefulness
Newborn
methods
Signal Processing
physiology
Asphyxia Neonatorum
physiology
classification
Computer-Assisted
Time Factors
Discriminant Analysis
Monitoring
physiology
methods
Automation
Motor Activity
Sleep
Electroencephalography
physiopathology
Probability
neunatal
Author
Johan Löfhede
Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering
Magnus Thordstein
University of Gothenburg
Nils Löfgren
Neoventa Medical AB
Anders Flisberg
University of Gothenburg
Manuel Rosa-Zurera
University of Alcalá
Ingemar Kjellmer
University of Gothenburg
Kaj Lindecrantz
Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering
Journal of Neural Engineering
1741-2560 (ISSN) 17412552 (eISSN)
Vol. 7 1 016007Subject Categories
Medical Laboratory and Measurements Technologies
Physiology
DOI
10.1088/1741-2560/7/1/016007