CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Automatic classification of background EEG activity in healthy and sick neonates

Johan Löfhede (Institutionen för signaler och system, Medicinska signaler och system) ; Magnus Thordstein ; Nils Löfgren ; Anders Flisberg ; Manuel Rosa-Zurera ; Ingemar Kjellmer ; Kaj Lindecrantz (Institutionen för signaler och system, Medicinska signaler och system)
Journal of Neural Engineering (1741-2560). Vol. 7 (2010), 1,
[Artikel, refereegranskad vetenskaplig]

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%.

Nyckelord: neunatal, EEG, signal processing, classification, Asphyxia Neonatorum, physiopathology, Automation, Brain, physiology, physiopathology, Discriminant Analysis, Electroencephalography, methods, Humans, Infant, Newborn, Linear Models, Monitoring, Physiologic, methods, Motor Activity, physiology, Probability, Signal Processing, Computer-Assisted, Sleep, physiology, Time Factors, Wakefulness, physiology



Denna post skapades 2010-01-26. Senast ändrad 2011-09-26.
CPL Pubid: 110884

 

Läs direkt!


Länk till annan sajt (kan kräva inloggning)


Institutioner (Chalmers)

Institutionen för signaler och system, Medicinska signaler och system
Institutionen för neurovetenskap och fysiologi (GU)

Ämnesområden

Medicinsk teknik
Klinisk neurofysiologi

Chalmers infrastruktur