## 4 Methods
### Performance Evaluation Metrics  
(1) K-fold cross-validation for the deep CNN-based classifiers  
The performances of the WS-CNN, WF-CNN and 1D-CNN classifiers were evaluated using an eight-fold cross-validation over datasets from eight preterm neonates. Typically, k-fold cross validation is used within a single dataset where the entire dataset is sub-partitioned. Here as in our previous works, in [14, 27], we perform cross-validation across 8 infants dataset where the data from each 7 infants represents a sub-partition for training and validation while data from an unseen infant is used for testing of the classifier. Results of the 8-fold cross-validation [63] could implicitly specify whether there is a significant variation in the profile of the HI sharp waves (assumed to be caused by drugs/treatments) across all babies. In other words, training of the networks over k-1 babies (seven) and testing that on the data from an unseen infant (one infant only) could determine the degree of reliability of the proposed pattern classifiers in the identification of HI sharp waves (Figure 4 and Table 2). This cross-validation procedure was repeated eight times across data from eight babies by swapping the test dataset at each time. The reported performance metrics in this article, including sensitivity, selectivity, precision, and overall accuracy measures, are the average of the values (mean \(\pm\) std) calculated from eight evaluations. To assess the reliability of the classifier, the performance of the net was evaluated through two different strategies:  
_Performance evaluation over the entire six hours clinical EEG across all eight babies (total of 48 h recordings)_: In this approach, data from the entire 6 h of seven babies (42 h) were used for training and validation of the net and data from the entire 6 h of the remaining infant was used to test the classifier. This approach was permutated in a similar manner across all eight babies (8-fold cross-validated) and provides a larger scale performance evaluation over much larger datasets. This approach was also further assessed by investigating the effects of reducing the number of convolutional layers on the overall performance of the classifier.  
_Performance evaluation over data from three EEG sub-sections of 2 hourly windows during the first hours of recordings_: This strategy has some benefits that allow for potential matching between early hours of neonatal EEG and post HI experimental recordings. However, birth is not necessarily aligned with the time of insult in our fetal sheep HI experiments, and HI could often happen pre-birth in the womb. In other words, we do not know if the babies have been exposed to hypoxia (or if they have, then to what extent). Also, complexities around the time of birth for preterm babies (i.e., resuscitation) might have caused delays in the initiation of the recordings. Thus, the chosen early 6 h from clinical data does not mimic the reported latent phase in our animal experiments. Moreover, many of the babies receive drugs/special treatments, which we would have assumed causing profile change of the epileptiform seizure-like events within the early hours from birth (i.e., HI micro-scale sharp waves). Therefore, dividing the first 6 h recordings into 2 h sub-windows will helpto assess the performance of the WS-CNN pattern classifier over smaller windows and to check if there is a drop in the overall performance due to the variations in the profile.
2. K-fold cross-validation for the Wavelet-Type-II-FLC  
The performance of the WT-Type-II-FLC classifier approach was also assessed using measures from the 8-fold cross-validation strategy as described in [14]. The WT transformed \"footprint of uncertainty\" (FOU) profiles of the actual sharp waves, from 7 babies excluding one at each set, were used to identify the sharp waves within the entire 6 h EEG of the remaining unseen infant (i.e., infant #3) (Figure S9i-p of Supplementary Materials).