Manuel Fernández-Delgado

TL;DR: The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in theTop-20, respectively).

TL;DR: In this article, a comparison of a large collection composed by 77 popular regression models which belong to 19 families: linear and generalized linear models, generalized additive models, least squares, projection methods, LASSO and ridge regression, Bayesian models, Gaussian processes, nearest neighbors, regression trees and rules, neural networks, bagging and boosting, deep learning and support vector regression is presented.

TL;DR: A new TU complex detection and characterization algorithm that consists of two stages, including the inclusion of U-wave characterization and a mathematical modeling stage, that avoids many of the problems of classic techniques when there is a low signal-to-noise ratio or when wave morphology is atypical.

TL;DR: A new artificial neural network model aimed at the morphological classification of heartbeats detected on a multichannel ECG signal with the capacity to dynamically self-organize its response to the characteristics of the ECG input signal is described.

TL;DR: A study shows the viability of a new technique for the diagnosis and monitoring of myocardial ischemia that is based on the utilization of heart-rate variability (HRV) information.