Quantitative continuity feature for preterm neonatal EEG signal analysis

Abstract

Electroencephalography (EEG) is an electrical signal recorded from a person's scalp, and is used to monitor the neurological state of the patient. This thesis proposes a quantified continuity feature to aid preterm neonatal EEG analysis. The continuity of EEG signals for preterm infants refers to the variation of the EEG amplitude, and is affected by the conceptional age of the infants. Currently, the continuity of the signal is determined largely by visual examination of the raw EEG signal, or by using general guidelines on amplitude-integrated EEG (aEEG), which is a compressed plot of the estimated signal envelope.

The proposed parametric feature embodies the statistical distribution parameters of the signal amplitudes. The signal is first segmented into pseudo-stationary segments using Generalized Likelihood Ratio (GLR). These segments are used to construct a vector of amplitude, the distribution of which can be modelled using a log-normal distribution. The mean and standard deviation of the log-normal distribution are used as the continuity feature. This feature is less prone to the effects of local transient activities than the aEEG.

This investigation has demonstrated that the degree of continuity corresponds to the major axis of the feature distribution in the feature space, and the minor axis roughly corresponds to the age of the infants in healthy files. Principal component analysis was performed on the feature, with the first coefficient used as a continuity index and the second coefficient as a maturation index. In this research, classifiers were developed to use the continuity feature to produce a qualitative continuity label. It was found that using a linear discriminant analysis based classifier, labelled data can be used as training data to produce labels consistent across all recordings. It was also found that unsupervised classifiers can assist in identifying the intrinsic clusters occurring in the recordings.

It was concluded that the proposed continuity feature can be used to aid further research in neonatal EEG analysis. Further work should focus on using the continuity information to provide a context for further feature extraction and analysis.