## 5 Results  
### Cross Dataset Results of the WS-CNN Classifier  
Table 3 compares the performance results of the proposed classifiers in the current article. The consistent high performance result of 99.34 \(\pm\) 0.51% across 6 h windows from several babies confirms the reliability of the developed WS-CNN classifier for the identification and classification of the HI sharp wave patterns in the 256 Hz clinical EEG. Results of the confusion matrix from 8-fold cross-validation in Table 3 indicate high sensitivity, selectivity, and precision measures of 99.66 \(\pm\) 0.35%, 98.97 \(\pm\) 1.17%, and 99.00 \(\pm\) 1.12%, respectively, using data from the entire 6 h EEG recordings across all eight babies. Also, consistent high performances of 98.5-99.1% obtained from the shorter 2-h windows, over the first 0-2 h, 2-4 h, 4-6 h sections of each infant (Tables S6-S8 of Supplementary Materials), confirming the considerable capability of the developed pattern classifier. The classifier was able to desirably identify sharp waves regardless of their positive or negative polarity. In our preliminary work, we had previously estimated that the proposed WS-CNN classifier would be able to perform well on the 256 Hz clinical data through testing of the classifier over 256 Hz down-sampled recordings from the original 1024 Hz animal EEGs [28]. Therefore, results of the current work from the actual 256 Hz clinical datasets validate the previously reported estimations.  
We also investigated the effects of reducing the original depth of 17 layers (14 convolutional layers) down to 13, 9 and 7 layers (corresponding to 10, 6 and 4 convolutional layers, respectively), using 8-fold cross-validation over the entire 6 h data across all babies (48 h total). This provided overall accuracies of 99.14 \(\pm\) 0.65%, 98.73 \(\pm\) 0.87%, and 97.81 \(\pm\) 1.29% for the 13, 9 and 7 layers structures, respectively, while the median value of  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Strategy** & **No. of Layers** & **Sensitivity (\%)** & **Selectivity (\%)** & **Precision (\%)** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{WS-CNN} & 17-layers & 99.66 \(\pm\) 0.35 & 98.97 \(\pm\) 1.17 & 99.00 \(\pm\) 1.12 & 99.34 \(\pm\) 0.51 \\  & 13-layers & 99.61 \(\pm\) 0.30 & 98.65 \(\pm\) 1.54 &  
the 17 layers structure was higher than other architectures (Figure 5i, also Tables S9-S11 in Supplementary Materials). Results indicated a fair reduction in the total accuracy of the net, while standard deviation increases as the number of layers decreases (Figure 5i). Also, the accuracy reduction using the lower number of layers in Figure 5i includes outliers that highlight much less accurate results in some of the babies. The ROC curves and the corresponding AUC values in Figure 5a-h provide visualization on how the performance of the WS-CNN classifier change across eight babies as the number of layers decreases. It is assumed that the slight reduction in the overall performance using a lower number of convolutional layers is due to the fine-tune/design of the inner convolutional layers in the smaller architectures. In other words, the image size reduction in the smaller network architectures was carefully re-designed to avoid huge image size reductions. Results of the WS-CNN classifier is still higher and much more reliable compared to our previous algorithms for the identification of HI sharp waves in sheep models.