Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this event in the earliest possible stage. This work presents a novel methodology to predict when a person can develop an SCD episode before it occurs. It is based on the adroit combination of the empirical mode decomposition, nonlinear measurements, such as the Higuchi fractal and permutation entropy, and a neural network. The obtained results show that the proposed methodology is capable of detecting an SCD episode 25 min before it appears with a 94% accuracy. The main benefits of the proposal are: (1) an improved detection time of 25% compared with previously published works, (2) moderate computational complexity since only two features are used, and (3) it uses the raw ECG without any preprocessing stage, unlike recent previous works.
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%.
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