Epilepsy prediction through optimized multidimensional sample entropy and Bi-LSTM

TL;DR: In this article , the authors presented two new hybrid predictors for forecasting oil prices based on recurrent neural networks using variational modal decomposition (VMD), sample entropy (SE), and gated recurrent units (GRUs).

TL;DR: A comprehensive overview of DL methods employed for epileptic seizure detection and prediction using neuroimaging modalities is presented in this paper , where a comparison has been carried out between DL-based CADS and DL models.

TL;DR: In this paper , a combination forecasting model based on Long Short-Term Memory (LSTM) network and BP neural network is designed to deal with nonlinear wind speed prediction.

TL;DR: This research presents a novel model named DE-CNN-BiLSTM deeply integrating the complexity of EEG signals, the spatial structure of brain and temporal contexts of emotion formation, and the new model can decode the EEG signal deeply and extract key emotional features to improve accuracy.

TL;DR: Principal component analysis (PCA) as discussed by the authors is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables, and its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and display the pattern of similarity of the observations and of the variables as points in maps.

TL;DR: A critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms, and point towards possible future developments and propose methodological guidelines for future studies on seizure predictions.

TL;DR: The lives of most people with epilepsy continue to be adversely affected by gaps in knowledge, diagnosis, treatment, advocacy, education, legislation, and research and Concerted actions to address these challenges are urgently needed.

TL;DR: The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature.