## 6 Conclusions  
At first sight, ECG records of a healthy person and a person with a risk of suffering an SCD event appear identical. Hence, it is not possible to detect significant differences between both registries with simple and direct comparisons. In this regard, it is imperative to develop a technique that can detect reliable changes in the ECG signal in order to associate them with a person with normal cardiac rhythm and a person at risk of presenting an SCD event. In this research, a methodology based on EMD algorithms and CNNs was designed to predict 30 min prior to an SCD event by using the ECG signals provided by the MIT/BIH-SCDH and MIT/BIH-NSR databases. The obtained results show that the combination of the CEEMD algorithm with a CNN provides the best predictions of an SCD event, reaching 97.5% of accuracy until 30 min before the event. Although the settings of the proposed methods are not strictly optimized since an optimization algorithm is not used, different parameters were tested in order to provide a suitable solution in terms of computational complexity and accuracy. Thus, the first six IMFs in the signal decomposition stage, a size of 164 \(\times\) 122 pixels for their image representation, and a CNN with the following structure: one convolutional and max pooling layers with five filters of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work** & **Signal** & & **Methods** & **Prediction Time/ Accuracy** \\ \hline [PERSON] et al. (2015) [12] & ECG & & 18 nonlinear features & 92.11\%/4 min \\  & & & Support vector machine & \\ \hline [PERSON] et al. (2018) [15] & HRV & & Pan-Tompkins and two nonlinear features & 95\%/6 min \\  & & & Decision tree & \\ \hline [PERSON] et al. (2018) [14] & ECG & & Wavelet packet transform and homogeneity index & 95.8\%/20 min \\  & & & Enhanced probabilistic neural network. & \\ \hline [PERSON] et al. (2020) & ECG & & Empirical mode decomposition and Higuchi fractal with permutation entropy & 94\%/25 min \\  & & & Multilayer perceptron & \\  & & & network & \\ \hline [PERSON] et al. (2021) [17] & ECG & & Recurrence complex network CNN & 90.6\%/- \\ \hline [PERSON] et al. (2022) [18] & ECG & & Wavelet packet transform CNN & 95.89\%/30 min \\  & & & CNN & \\ \hline Proposed work & ECG & & CEEMD CNN & 97.1\%/30 min \\ \hline Not reported. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: A quantitative and qualitative comparison between the proposal and similar works.  
3 \(\times\) 3 and training with a batch number of 64 and 10 epochs, were enough to obtain the previously mentioned results.  
As the obtained results are considered preliminary due to the limited databases, in future works, the proposal must be tested and calibrated with larger databases that include more patients and other rhythm abnormalities. In addition, it is proposed to carry out a hardware implementation based on a field-programmable gate array (FPGA) in order to exploit its parallelism to perform the IMFs' extraction and the CNN-based prediction, thus providing a portable solution that contributes to the prediction of SCD from a technological point of view.  
Conceptualization, M.A.C.-B., J.P.A.-S. and M.V.-R.; methodology, M.A.C.-B., A.H.R.-R. and M.V.-R.; software, M.A.C.-B. and A.H.R.-R.; formal analysis, resources, and data curation, M.A.C.-B., A.H.R.-R. and A.V.P.-S.; writing--review and editing, all authors; supervision, project administration, and funding acquisition, J.P.A.-S., D.G.-L. and M.V.-R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are not publicly available due to privacy issues.  
This work was partially supported by the Mexican Council of Science and Technology (CONACyT) by the scholarships 830903, 826907, and 814956.  
The authors declare no conflict of interest.