The timing of hypoxia-ischemia (HI) in preterm infants is often uncertain and there are few biomarkers to determine whether infants are in a treatable stage of injury. We evaluated whether epileptiform sharp waves recorded from the parietal cortex could provide early prediction of neuronal loss after HI. Preterm fetal sheep (0.7 gestation) underwent acute HI induced by complete umbilical cord occlusion for 25 minutes (n = 6) or sham occlusion (control, n = 6). Neuronal survival was assessed 7 days after HI by immunohistochemistry. Sharp waves were quantified manually and using a wavelet-type-2-fuzzy-logic-system during the first 4 hours of recovery. HI resulted in significant subcortical neuronal loss. Sharp waves counted by the automated classifier in the first 30 minutes after HI were associated with greater neuronal survival in the caudate nucleus (r = 0.80), whereas sharp waves between 2–4 hours after HI were associated with reduced neuronal survival (r = −0.83). Manual and automated counts were closely correlated. This study suggests that automated quantification of sharp waves may be useful for early assessment of HI injury in preterm infants. However, the pattern of evolution of sharp waves after HI was markedly affected by the severity of neuronal loss, and therefore early, continuous monitoring is essential.

The presence of abnormal infant General Movements (GMs) is a strong predictor of progressive neurodevelopmental disorders, including cerebral palsy (CP). Automation of the assessment will overcome scalability barriers that limit its delivery to at-risk individuals. Here, we report a robust markerless pose-estimation scheme, based on advanced deep-learning technology, to track infant movements in consumer mobile device video recordings. Two deep neural network models, namely Efficientnet-b6 and resnet-152, were trained on manually annotated data across twelve anatomical locations (3 per limb) in 12 videos from 6 full-term infants (mean age = 17.33 (SD 2.9) wks, 4 male, 2 female), using the DeepLabCut™ framework. K-fold cross-validation indicates the generalization capability of the deep-nets for GM tracking on out-of-domain data with an overall performance of 95.52% (SD 2.43) from the best performing model (Efficientnet-b6) across all infants (performance range: 84.32–99.24% across all anatomical locations). The paper further introduces an automatic, unsupervised strategy for performance evaluation on extensive out-of-domain recordings through a fusion of likelihoods from a Kalman filter and the deep-net. Findings indicate the possibility of establishing an automated GM tracking platform, as a suitable alternative to, or support for, the current observational protocols for early diagnosis of neurodevelopmental disorders in early infancy.

Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

Alongside clinical achievements, experiments conducted on animal models (including primate or non-primate) have been effective in the understanding of various pathophysiological aspects of perinatal hypoxic/ischemic encephalopathy (HIE). Due to the reasonably fair degree of flexibility with experiments, most of the research around HIE in the literature has been largely concerned with the neurodevelopmental outcome or how the frequency and duration of HI seizures could relate to the severity of perinatal brain injury, following HI insult. This survey concentrates on how EEG experimental studies using asphyxiated animal models (in rodents, piglets, sheep and non-human primate monkeys) provide a unique opportunity to examine from the exact time of HI event to help gain insights into HIE where human studies become difficult.