## 4 Experimentation and Results
### CNN Performance  
To obtain a CNN structure with low computation resources but high accuracy, different configurations were tested. As the first test, the CNN performance for different sizes of images obtained by the 3 methods, i.e., EMD, EEMD, and CEEMD, is compared. The original image size is 218 \(\times\) 162 pixels, where 3 smaller sizes (i.e., 164 \(\times\) 122, 131 \(\times\) 98, and 55 \(\times\) 41 pixels) are analyzed to find the best relationship between accuracy and computational load based on the size of the images. The smaller the image size, the lower the number of mathematical operations required in the CNN. The first row of Figure 8 shows the images depicted in Figure 7. The left side of Figure 8 shows the sizes of the images that were used for the comparison. The image size represents the amount of data that enters a CNN; therefore, a reduction in its size reduces the computational load, but it can also reduce the accuracy of the CNN if most of the information is lost during the compression process. In Figure 8, all the images were enlarged to have the same visual size and observe how the patterns are somehow smoothed due to the compression process. To perform the comparative equitably, the same CNN structure has to be used. In the beginning, the proposed CNN contains a five-filter convolution layer with a size of 5 \(\times\) 5 and a pooling layer with a 2 \(\times\) 2-size filter as shown in Figure 9. Table 2 contains detailed information on the CNN used. Although the previously mentioned values could be randomly selected, they are established according to the experience of the authors of this work. The results for other values that improve the CNN performance will be shown later. Once the first CNN was configured, its training and validation were done with each of the methods and each of the image sizes shown in Figure 8. It is worth noting that the dataset consists of 540 healthy images (18 patients \(\times\) 30 windows of 1 min) and 600 images with a future SCD condition (20 patients \(\times\) 30 windows of 1 min), where 432 images from each condition (i.e., 80% and 72% of data, respectively) are used for training and the remaining images (i.e., 20% and 27% of data, respectively) are used for validation. Figure 7: Top view of the surface graphics for the set of IMFs of a (**a**) 1 min segment of a healthy patient and (**b**) 1 min before the SCD event. Figure 10 shows the accuracy obtained from the CNN applied to each of the different image sizes and the preprocessing methods. This figure shows that the image with the largest size presents the best accuracy for the three methods, which is somehow expected because the analyzed images have the greatest amount of data. On the contrary, the worst \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Type** & **Activations** & **Learnable** & **Total Learnable** \\ \hline imageinput & Image Input & \(162\times 218\times 3\) & & 0 \\ conv & Convolution & \(162\times 218\times 5\) & Weights \(5\times 5\times 3\times 5\) & 380 \\ batchnorm & Batch & & Bias \(1\times 1\times 3\) & 380 \\  & Normalization & \(162\times 218\times 5\) & Offset \(1\times 1\times 5\) & 10 \\ Relu & ReLU & \(162\times 218\times 5\) & - & 0 \\ maxpool & Max Pooling & \(161\times 217\times 5\) & - & 0 \\ Fc & Fully & & Weights \(2\times 174685\) & 349, 372 \\  & Connected & \(1\times 1\times 2\) & Bias \(2\times 1\) & 349, 372 \\ softmax & SoftMax & \(1\times 1\times 2\) & - & 0 \\ classoutput & Classification & - & - & 0 \\  & Output & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: First CNN configuration. Figure 8: Image size comparison for (**a**) images of a healthy patient and (**b**) images 1 min before the SCD. Figure 9: Base convolutional neural network (CNN) architecture. results are obtained for a size of 55 \(\times\) 41 pixels. Despite these results, it is worth noting that the three methods offer results higher than 85% of accuracy.