Search Results for author:
Found 5 papers, 4 papers with code
End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks
Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovascular diseases such as stroke and heart failure.
Invertible Kernel PCA with Random Fourier Features
Kernel principal component analysis (kPCA) is a widely studied method to construct a low-dimensional data representation after a nonlinear transformation.
Deep networks for system identification: a Survey
For this reason, we provide a survey of deep learning from a system identification perspective.
ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods
We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations.
Deep State Space Models for Nonlinear System Identification
Deep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs.
