Automatic detection of preterm neonatal EEG background states

Background states of an EEG signal describe the distinctive variations in the amplitude of the signal with respect to time. Background state detection in EEG is used to help estimate the brain growth progress in infants. Currently, background detection is mostly done manually, which is highly subjective. This paper proposes a way to automatically detect background states for preterm infants. The distribution of the amplitude vector in a 10-minute window of 2-channel preterm neonatal EEG signal is analysed, and the mean and standard deviations of the amplitudes in log-space are used as features in a linear discriminant analysis based classifier. The results are compared with existing methods of background detection. The algorithm performs well compared with the visual classification. It also shows less sensitivity to local variations the existing algorithm are suffering from.