ECG Segmentation using a Neural Network as the Basis for Detection of Cardiac Pathologies

Abstract

Electrocardiography allows fast and noninvasive diagnosis and screening of a wide range of cardiac diseases. The interpretation of ECGs is difficult, and depends on the levels of training of the physician. In consequence, pathologies can remain undiagnosed or norm-variations are interpreted as pathological. The PhysioNet/Computing in Cardiology Challenge 2020 aims to classify various cardiac pathologies in 12- lead ECGs [1, 2], data was collected across a variety of different clinics and countries to pave the way for a common evaluation of ECGs. Our Team Heartly-AI proposes a two step algorithm using a UNet and XGBoost for the 2020 PhysioNet Computing in Cardiology Challenge ”Classification of 12 lead ECGs”. We scored 0.159 on the official testset and ranked about 199th out of the 200 teams that participated in this year’s Challenge.

Tasks

Reproductions