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Adaptive probabilistic modelling of the EEG background is proposed for seizure detection in neonates with hypoxic ischemic encephalopathy. The decision is made based on temporal derivative of the seizure probability with respect to the adaptively modeled level of background activity. The robustness of the system to long duration, seizure-like artifacts (in particular those due to respiration) is improved. The results are validated on the largest available clinical dataset, comprising 816.7 hours. By exploiting the proposed adaptation, the ROC area is significantly increased for patients with EEG corrupted with respiration artifact, with the average increase of 20% (relative) across all patients. [ABSTRACT FROM PUBLISHER]

We propose a novel framework for longitudinal registration which can handle large intra-subject anatomical variations. The framework exploits freely available spatio-temporal atlases, which can aid the longitudinal registration process as it provides prior information about the missing anatomical evolution between two scans taken over large time-interval. The spatio-temporal atlas is used to develop an approach to carry out longitudinal registrations via atlas propagation. We carried out evaluation experiments using 50 neonatal subjects with two scans taken at different time-intervals. We used different metrics, including intensity similarity and segmentation overlap, to assess the registration performance. The proposed registration framework outperforms direct registration by providing an accurate and consistent registration, particularly when the time-interval between scans increases. We refer to our approach as a framework for Longitudinal Image registration via Spatio-temporal Atlases (LISA). [ABSTRACT FROM PUBLISHER]