Graphical analysis of the progression of atrial arrhythmia using recurrent neural networks

Abstract

Pacemaker logs are used to predict the progression of paroxysmal cardiac arrhythmia to permanent atrial fibrillation by means of different deep learning algorithms. Recurrent Neural Networks are trained on data produced by a generative model. The activations of the different nets are displayed in a graphical map that helps the specialist to gain insight into the cardiac condition. Particular attention was paid to Generative Adversarial Networks (GANs), whose discriminative elements are suited for detecting highly specific sets of arrhythmias. The performance of the map is validated with simulated data with known properties and tested with intracardiac electrograms obtained from pacemakers and defibrillator systems.

Nahuel Costa, PhD

Assistant professor and Machine Learning researcher 🤖