## 6 Conclusions  
This article highlights the exceptional capabilities of convolutional neural network (CNN)-based pattern classifiers, fed with high-resolution wavelet scalogram feature maps, in accurately identifying HI sharp wave biomarkers in a large dataset of 256 Hz sampled EEG from human preterm infants at birth. The remarkable classification accuracy of 99.34 \(\pm\) 0.51% has an impact by indicating the considerable ability of the classifier in the identification of translational sharp wave EEG patterns in the challenging clinical sampling frequency of 256 Hz within the most clinically important first hours of life. The proposed classifier was tested over 13,624 wavelet scalogram images along 48 h of raw EEG data and was able to accurately identify patterns regardless of the type of treatment or drug that the babies had received (this was originally assumed to highly influence the morphology of the patterns, highly). It was shown that the WS-CNN pattern classifier outperforms the WF-CNN, the conventional 1D-CNN and wavelet-fuzzy approaches. Overall, whilst the WS-CNN performs best among all, the other purposed techniques come very close within the error margin, allowing user to choose a strategy based on their computational requirements. The introduced automatic strategy is a big step ahead toward early diagnosis of HI injury in at-risk infants, at bed-side, through real-time identification of EEG biomarkers at birth, where therapeutic protocols have been shown to be optimally neuroprotective in. Signal processing-wise, the introduced method is quite generic and can be re-designed for various applications in many different fields.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/signals5020014/s1](https://www.mdpi.com/article/10.3390/signals5020014/s1)]([https://www.mdpi.com/article/10.3390/signals5020014/s1](https://www.mdpi.com/article/10.3390/signals5020014/s1)).  
Conceptualization, H.A., L.B., A.J.G. and M.R.B.; Methodology, H.A., L.B., A.J.G., M.R.B., R.B. and D.R.; Software, Validation, Algorithms Development, Manual Annotation, and Formal analysis, H.A.; Investigation, L.B., A.J.G., M.R.B. and R.B.; Resources, H.A., L.B., A.J.G. and M.R.B.; Data curation, H.A.; Writing--original draft, H.A.; Writing--review and editing, H.A., L.B., A.J.G. and M.R.B.; Visualization, H.A.; Supervision, L.B. and A.J.G.; Funding acquisition, L.B. and A.J.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Health Research Council of New Zealand. Grant numbers: HRC 17/601, HRC 18/225, and HRC 22/559.  
The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author/s. The raw data supporting the conclusions are unavailable due to ethical restrictions.  
The research was supported by grants from the Health Research Council of New Zealand (HRC-17/601, HRC 18/225, and HRC 22/559). The authors would also like to acknowledge the use of the New Zealand eScience Infrastructure (NeSI) high-performance computing facilities for the results of this research. URL: [[https://www.nesi.org.nz](https://www.nesi.org.nz)]([https://www.nesi.org.nz](https://www.nesi.org.nz)) (access date: 20 April 2024).  
The authors declare no conflicts of interest.