## 1 Introduction  
Hypoxic-Ischemic (HI) brain injury due to lack of cerebral oxygen at birth is shown to be followed by a critically short window of opportunity for potential treatments in newborn babies [1, 2]. Premature babies are at much higher risks of neurological injury due to HI [3, 4, 5]. Uncontrolled Hypoxic-Ischemic Encephalopathy (HIE) evolves from a few hours to days after birth resulting in severe neurodevelopmental impairments, disability, and death [6]. In term neonates, therapeutic hypothermia (TH: brain cooling) can optimally stop the spread of brain damage only if initiated within the very early 2-3 h of birth [7, 8]. Conditions are often more complex for preterm infants for whom hypothermia is not applicable, and who seldom exhibit symptoms due to their frailty [9, 10]. Hence, there is an urgent need to identify reliable prognostic biomarkers that enable the development of automated strategies for swiftly diagnosing the injury before it becomes irreversible. This will ensure the implementation of the appropriate type of treatment at the right time. Our post HI monitoring of raw Electroencephalogram (EEG) signals from clinically relevant in utero preterm fetal sheep models has permitted our group to identify the first HI biomarkers in the HI EEG, in the form of micro-scale sharp waves [11, 12, 13, 14], which correlate with injury [13], within the first few hours of HI at 1024 Hz sampled EEG background (refer to Figure 1a,c,d in reference [11]). We have recently demonstrated that translational sharp waves with similar morphology (r = 0.94) to those observed in fetal sheep after HI events are also present in the lower 256 Hz sampled clinical EEG of extremely preterm neonates during the first 6 h of life (refer to Figures 1b,e,f, 2 and 3 in reference [11] for visual representations). Unlike experimental models where the start of trial is known, in clinical practice the exact timing of pre-birth HI is unknown and is often challenging to be determined [15]. In other words, the exact time of birth is not necessarily aligned with the time of occlusion, as can be achieved in controlled animal experiments (refer to Figure 1a,b in reference [11] for visual representations). This implies that the brain injury may have already well-evolved beyond the window of opportunity for the available treatments [8,16]. Hence, the necessity of developing reliable automatic algorithms for real-time identification of HI sharp waves, as potential early biomarkers of an evolving HI brain injury [11,13], becomes even more critically important. Recent advances in machine learning strategies and in particular the use of robust convolutional neural network (CNN) architectures have been beneficial to HI research for the detection of clinical neonatal EEG seizures and grading [17, 18, 19, 20] as well as epileptic EEG discharges [21]. However, current automated strategies in clinical HI studies are mostly concerned with determining the degree of HI through classification of EEG segments as well as automatic identification of high amplitude seizures after the window of opportunity has passed [22, 23, 24, 25, 26]. From animal studies we know that the high amplitude seizures mark the close of the window of opportunity for treatment (refer to Figure 1a of reference [11]) [15]. Therefore, accurate identification of early signatures of HI brain injury (i.e., micro-scale sharp waves) within the very first hours of birth could provide the opportunity for an effective diagnosis of HI injury and will help to optimize manual interventions. Our research group has previously reported several successful fusion techniques for the precise identification of sharp wave patterns [11,13,14] and HI gamma spike transients [27] in the HI EEG of preterm fetal sheep, during the early 6-7 h 'latent-phase' of the injury post-HI.