###### Abstract  
Sudden cardiac death (SCD) is a global health problem, which represents 15-20% of global deaths. This type of death can be due to different heart conditions, where ventricular fibrillation has been reported as the main one. These cardiac alterations can be seen in an electrocardiogram (ECG) record, where the heart's electrical activity is altered. The present research uses these variations to be able to predict 30 min in advance when the SCD event will occur. In this regard, a methodology based on the complete ensemble empirical mode decomposition (CEEMD) method to decompose the cardiac signal into its intrinsic mode functions (IMFs) and a convolutional neural network (CNN) for automatic diagnosis is proposed. Results for the ensemble empirical mode decomposition (EEMD) method and the empirical mode decomposition (EMD) method are also compared. Results demonstrate that the combination of the CEEMD and the CNN is a potential solution for SCD prediction since 97.5% of accuracy is achieved up to 30 min in advance of the SCD event. electrocardiogram signal; sudden cardiac death; empirical mode decomposition; ensemble empirical mode decomposition; complete ensemble empirical mode decomposition; convolutional neural network +
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a discrete wavelet transform, 18 nonlinear features (i.e., Hurst's exponent, approximate entropy, sample entropy, etc. ), and a support vector machine to forecast an SCD. The authors reported that their method could predict an SCD 4 min before it occurs with 92.11% of accuracy. [PERSON] et al. [14] presented a machine learning scheme based on the integration of wavelet packet transform, homogeneity indicator, and an enhanced probabilistic neural network classifier for predicting an SCD event utilizing ECG signals provided in the database mentioned above. The authors demonstrated that their proposal could predict an SCD 20 min before its onset. [PERSON] et al. [15] investigated the forecast of an SCD episode utilizing the heart rate variability (HRV) from the ECG signals employing the Pan-Tompkins algorithm. The obtained HRV signals were analyzed by a recurrence quantification analysis and the increment entropy to discover features in the signal. A decision tree is used to classify these characteristics with a Euclidean distance. They established that their proposal has an accuracy of 95% to forecast an SCD episode 6 min before its onset. In addition, [PERSON] [16] integrated the empirical mode decomposition with fractal dimension (i.e., Katz, Higuchi, and box dimension indices) and entropy (Shannon and permutation entropy indices) in combination with an artificial neural network known as multilayer perceptron classifier to predict an SCD event. The investigators described that their proposal is capable of predicting an SCD 25 min before its onset with 94% of accuracy. On the other hand, in recent years, the use of deep learning architectures for predicting an SCD event has been introduced. For instance, [PERSON] et al. [17] combined a recurrence complex network with a convolutional neural network (CNN) to predict an SCD episode, obtaining 90.60% of accuracy. [PERSON] & [PERSON] [18] integrated a wavelet packet transform with a CCN to predict an SCD event 30 min before its commencement. The authors indicated that 95.89% of accuracy in forecasting an SCD is reached. In these works, CNNs are used because they do not need human supervision for the extraction of important features that allow the pattern recognition task. Although the works mentioned above report promising results to forecast an SCD, there are some opportunities for improvement, e.g., (1) increase the time window to forecast an SCD, (2) reduce the complexity and computational resources of the method, and (3) improve the prediction accuracy.