Neonatal seizure localization using PARAFAC decomposition

Abstract

The description and evaluation of two EEG-based algorithms for automatic and objective determination of the seizure location in the neonatal brain as it is reflected on the scalp. Each algorithm extracts the electrical potential distribution of the seizure over the scalp using the higher-order canonical decomposition or Parallel Factor Analysis (PARAFAC), also referred to as the CP model. This model decomposes a tensor in a sum of rank-1 components. The two algorithms differ in the way the tensor is constructed and in the type of activity they are able to extract. While the first method extracts oscillatory seizure activity, the second extracts spike train activity. We compared the seizure localization results of 21 seizures from 6 neonates with post-asphyxial hypoxic ischemic encephalopathy, with that based on the visual analysis of the EEG by a clinical neurophysiologist. There was a good agreement between the two methods in the localization of seizure onset in all. The techniques presented in this paper are robust, objective methods to determine neonatal seizure localization. They can be a useful tool for neonatal EEG analysis and for continuous brain function monitoring. The proposed algorithms significantly improve neonatal seizure localization and monitoring.