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

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 pathol...

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Bibliographic Details
Main Authors: Sodmann, Philipp, Vollmer, Marcus
Format: Conference Proceeding
Language:English
Subjects:
Cardiology
Electrocardiography
Medical services
Neural networks
Noninvasive treatment
Pathology
Training
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Summary: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, 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 Phys-ioNet Computing in Cardiology Challenge "Classification of 12 lead ECGs". The algorithm achieved a 5-fold cross-validation metric of 0.113 and scored 0.136 on the official test set, therefore placing us 28th out of the 41 teams in the official ranking.
ISSN:2325-887X
DOI:10.22489/CinC.2020.356