Epilepsy is a brain disorder that affects about 50 million persons around the world and is characterized by generating recurrent seizures, which can put patients in permanent because of falls, drowning, burns, and prolonged seizures that they can suffer. Hence, it is of vital importance to propose a methodology with the capability of predicting a seizure with several minutes before the onset, allowing that the patients take their precautions against injuries. In this regard, a methodology based on the wavelet packet transform (WPT), statistical time features (STFs), and a decision tree classifier (DTC) for predicting an epileptic seizure using electrocardiogram (ECG) signals is presented. Seventeen STFs were analyzed to measure changes in the properties of ECG signals and find characteristics capable of differentiating between healthy and 15 min prior to seizure signals. The effectiveness of the proposed methodology for predicting an epileptic event is demonstrated using a database of seven patients with 10 epileptic seizures, which was provided by the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH). The results show that the proposed methodology is capable of predicting an epileptic seizure 15 min before with an accuracy of 100%. Our results suggest that the use of STFs at frequency bands related to heart activity to find parameters for the prediction of epileptic seizures is suitable.

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.

Epilepsy, a neurological disorder, affects millions of persons worldwide. It is distinguished by causing recurrent seizures in patients, which can conduct to severe health problems. Consequently, it is essential to offer a method capable of timely predicting a seizure before its appearance, so patients can avoid possible injuries by taking preventive action. In this sense, a method based on the integration of discrete wavelet transform (DWT), fractal dimension, and support vector machine (SVM) is presented for the prediction of an epileptic seizure up to 30[Formula: see text]min before its onset through the analysis of electroencephalogram (EEG) signals. DWT is initially applied to the EEG signals to obtain their main neurological bands; then, five fractal dimension indices (e.g. Sevcik, Petrosian, Box, Higuchi, and Katz) are explored as potential seizure indicators. Finally, an SVM is developed to predict the epileptic seizure automatically. The effectiveness of the proposal to predict an epileptic crisis is validated by employing a database of 14 subjects with 42 epileptic seizures provided by the Massachusetts Institute of Technology and the Children’s Hospital Boston. The results demonstrate that the proposal can predict an epileptic seizure up to 30[Formula: see text]min before its onset with a high accuracy of 93.33%.