Development of an automated neonatal EEG seizure monitor

Brain function requires a continuous flow of oxygen and glucose. An insufficient supply for a few minutes during the first period of life may have severe consequences or even result in death. This happens in one to six infants per 1000 live term births. Therefore, there is a high need for a method which can enable bedside brain monitoring to identify those neonates at risk and be able to start the treatment in time. The most important currently available technology to continuously monitor brain function is electroEncephaloGraphy (or EEG). Unfortunately, visual EEG analysis requires particular skills which are not always present round the clock in the Neonatal Intensive Care Unit (NICU). Even if those skills are available it is laborsome to manually analyse many hours of EEG. The lack of time and skill are the main reasons why EEG is not widely used in the NICU although many involved agree it should be. The work presented in the current thesis aims at finding methods for automated analysis of neonatal EEG to facilitate its use in the NICU. In this thesis we focused on one of the most important treatable phenomena in neonatal EEG, namely neonatal seizures. Neonatal seizures are an important sign of central nervous system dysfunction and require immediate medical attention. The majority of neonatal seizures are subclinical, being detected only by EEG monitoring. Hence, there is scope for an automated EEG based seizure monitoring system. The most important topic covered by this thesis is automated seizure detection. We identified the two main types of neonatal seizures and developed an appropriate detection strategy for each by mimicking the human observer reading EEG. The methods were validated on a large dataset. An implementation of the seizure detection able to run in real-time has been developed and succesfully tested at the bedside. The EEG contains many artifacts of which some have similar morphology to neonatal seizures. These artifacts may lead to false positive detections by the seizure detector and therefore should be removed. We identified the most important artifacts in neonatal EEG leading to false positives and removed them using Independent Component Analysis (ICA). We quantify the benefit of artifact removal on seizure EEG by measuring the performance of the developed seizure detector with and without artifact preprocessing. As we are using the full 13 up to 17 channel EEG we have the ability to exploit the spatial resolution of the EEG. Therefore we developed two seizure localization methods based on Canonical / Parallel Factor Analysis (CPA) which are able to extract the spatial distribution of the seizure on the scalp. These distributions can be visualized to the user using topographic plots. Analysis of these plots leads to information about the depth of the seizure (cortical or subcortical number of seizure foci present, spread of seizures to contralateral hemisphere, etc. Especially for the target public of non-expert users of EEG, these topographic plots provide easy understanding of the spatial information contained in the EEG which would otherwise need years of training. Both methods are validated on a large dataset. In this thesis we also provide a proof of concept study in which these methods are combined with dipole source localization in a realistic head model. This technology can be used to study the relationship between seizure localization and the location of brain damage as seen on Magnetic Resonance Imaging (MRI). In the final chapter we propose three types of EEG monitors with increasing complexity that integrate the developed algorithms. Each of these would significantly improve neonatal seizure monitoring and hopefully we will see a commercial implementation in the future.