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%.
physiopathology
signal processing
physiopathology
Discriminant Analysis
Brain
physiology
Asphyxia Neonatorum
Signal Processing
Motor Activity
Sleep
Computer-Assisted
physiology
Time Factors
EEG
Wakefulness
methods
physiology
Humans
Automation
methods
physiology
Newborn
Electroencephalography
neunatal
classification
Physiologic
Monitoring
Infant
Probability
Linear Models
Author
Johan Löfhede
Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik
Magnus Thordstein
University of Gothenburg
Nils Löfgren
Neoventa Medical AB
Anders Flisberg
Goteborgs Universitet
Manuel Rosa-Zurera
Universidad de Alcala
Ingemar Kjellmer
Goteborgs Universitet
Kaj Lindecrantz
Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik
Journal of Neural Engineering
1741-2560 (ISSN)
Vol. 7 1Subject Categories (SSIF 2011)
Medical Laboratory and Measurements Technologies
Physiology
DOI
10.1088/1741-2560/7/1/016007