# Early Signatures of Brain Injury in the Preterm Neonatal EEG
###### Abstract
Thus, HAS should mainly be thought of as useful markers of secondary deterioration after acute brain injury. Figure 1: Examples of the changes in EEG power after hypoxia-ischemia induced by umbilical cord occlusion (UCO) in preterm fetal sheep (**a**) and during the first day of life after birth in an extremely preterm human neonate (**b**). Examples of post HI micro-scale sharp-waves along profoundly suppressed EEG in 1024 Hz recordings of preterm fetal sheep model (**c,d**) and in 256 Hz neonatal EEG from an at risk preterm infant (**e,f**). The HI sharp-waves are indicated with \({}^{\star\star}\). Our team has previously shown that in preterm fetal sheep these EEG waveforms emerge in the form of micro-scale sharp-waves and gamma spike transients, and that they are significantly correlated with the subcortical brain damage post HI injury [6; 15]. Our team has successfully developed and validated automated advanced signal processing technology, based on deep-learning, for the identification, quantification, and localization of these patterns in preterm fetal sheep data (accuracy > 99%) [15; 16]. We have previously shown that micro-scale sharp-waves, in particular, contain timing information related to the evolution of injury in the latent phase after HI. Following this, in fetal sheep the phase of secondary deterioration is associated with a fully stochastic EEG background regardless of age and that HAS in this phase are not necessarily associated with clinical signs. In the preterm brain, these seizures are most often discrete events, and they are less likely to show clinical seizures than term infants [9; 10; 17]. Real-time manual identification and assessments of EEG data is challenging and requires expert clinical knowledge [18; 19; 20; 21; 22]. This is even more challenging when subtle waveforms, often with complex morphological patterns, need to be quantified [23; 24]. Hence, there is a significant need for developing automated signal processing techniques to assist with reliable assessments of EEG in suspected cases of brain injury [4; 19; 25; 26; 27; 28; 29]. One approach is to focus on automatic identification and quantification of micro-scale EEG patterns as well as seizures in order to determine the timing and effectively target treatment for infants who might benefit [9; 15; 17; 25; 26; 30; 31]. The primary aim of this study was to explore the potential translational relevance of subtle EEG patterns during the first 6 h of life, specifically focusing on their presence and significance in clinical recordings obtained from extremely preterm newborns. By closely examining these EEG patterns in this vulnerable population, this study seeks to determine whether they exhibit consistent characteristics and whether these patterns could serve as reliable indicators of neurological development similar to those seen in our fetal sheep models. Therefore, this article will delve into whether the micro-scale sharp-waves observed in preterm fetal sheep models after HI are also evident within the initial 6 h of life in clinical recordings from extremely preterm newborns. The study will then discuss how these subtle waveforms may represent early signatures of preterm brain injury, and how automated algorithms could be useful for the identification and quantification of these patterns in clinical recordings.