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 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.
Code (0)
등록된 구현이 없습니다.
Tasks
Medical DiagnosisMedical Image SegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR
Myocardial infarction (MI) is a leading cause of death worldwide. Late gadolinium enhancement (LGE) and T2-weighted cardiac magnetic resonance (CMR) imaging can respectively identify scarring and edema areas, both of whi…
A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI
Automated cardiac segmentation from magnetic resonance imaging datasets is an essential step in the timely diagnosis and management of cardiac pathologies. We propose to tackle the problem of automated left and right ven…
Cardiac SegmentationManagementRight Ventricle SegmentationSegmentationWhen the clock strikes: Modeling the relation between circadian rhythms and cardiac arrhythmias
It has recently been observed that the occurrence of sudden cardiac death has a close statistical relationship with the time of day, viz., ventricular fibrillation is most likely to occur between 12 am-6 am, with 6 pm-12…
RelationRhythmAutomatic Myocardial Segmentation by Using A Deep Learning Network in Cardiac MRI
Cardiac function is of paramount importance for both prognosis and treatment of different pathologies such as mitral regurgitation, ischemia, dyssynchrony and myocarditis. Cardiac behavior is determined by structural and…
DiagnosticPrognosisSegmentationHow well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?
Planning the optimal time of intervention for pulmonary valve replacement surgery in patients with the congenital heart disease Tetralogy of Fallot (TOF) is mainly based on ventricular volume and function according to cu…