# Early Signatures of Brain Injury in the Preterm Neonatal EEG
## 4 Discussion  
### Insights on the Utility of Computer-Aided Diagnostic Algorithms  
EEG is a highly complex and detailed signal that requires experienced clinicians, pediatrics, or neonatal EEG specialists to interpret [32, 33]. Therefore, the current clinical utility of this technology at the neonatal intensive care units (NICUs) is limited to units with access to experts. EEG monitoring is also a critically time-consuming process that requires meticulous attention to detail, adding to the burden for human interpretation. Research shows that a simplified 2-4 channel EEG system can be more practical at NICUs [34]. Despite these limitations, EEG provides useful information related to the evolution of HI injury and could contain potential biomarkers related to evolution of HIE [6, 35]. Therefore, there is currently a growing interest in the application of automated algorithms for the assessment and grading of the post HI EEG background [4, 34]. However, there is limited evidence for how automated deep-learning-based algorithms could help to identify and quantify HI-related markers in the post-HI EEG; furthermore, there is a question as to what extent these strategies could translate across different stages of brain maturation.  
Studies show that the severity of neurological damage can be predicted based on the first 12 h EEG recordings from a two-channel EEG-set initiated within the early hours of birth [34, 36, 37, 38]. Importantly, in a cohort of term infants with HIE, TH was more efficacious if initiated within less than 3 h of birth [12]. Our team also has a successful track record on developing and validating advanced technology, based on deep-learning, for the identification, quantification, and localization of micro-scale EEG patterns in the latent phase of preterm fetal sheep (0-7 h post-HI) [9, 17, 25]. Our data-driven techniques include 1D Convolutional Neural Networks (1D-CNN), Wavelet-Fourier CNNs (WF-CNN), and a highly accurate state-of-the-art 2D Wavelet-Scalogram CNN (WS-CNN) approach that infuses spectrally rich feature maps of EEG sections into a deep CNN classifier for pattern recognition.  
Further, the literature indicates an extensive set of clinical and experimental work on the application of data-driven deep-learning-based classifiers for seizure identification in term and preterm infants [27, 39, 40, 41, 42] as well as adults [43]. Our team has also recently developed and validated a generalized seizure (HAS) detector algorithm, using data from fetal sheep, that can accurately identify post-HI stereotypic seizures regardless of age or use of TH [44]. HAS are common in clinical recordings and TH can partially suppress these seizures in both animals and human neonates [44, 45, 46, 47, 48]. Therefore, automated seizure detector algorithms hold clinical utility to help identify the secondary phase of injury (in some cases, albeit not all). However, HAS identification has almost no utility for detecting the latent phase of injury where the earlier initiation of therapeutic treatment, prior to the emergence of HAS, has been shown to be most effective. The strong morphological similarity between the clinical and experimental seizures makes our pre-clinically validated seizure detector, reported in [44], a good candidate for the identification of clinical seizures in the secondary phase (see Figure 1).  
The reliability of our pattern classifiers still needs to be tested on clinical EEG recordings for the identification of micro-scale sharp-wave and HAS. This is particularly important for micro-scale seizures occurring in the latent phase, as they are subject to higher complexities (Figure 3) and rapid morphological evolution, influenced by age and/or severity of neuronal loss.