Metrology of automated data analysis for cardiac arrhythmia management

Short Name: MedalCare, Project Number: 18HLT07

Validating software for automatic diagnoses of cardiovascular diseases

Image of heart beats on a ECG

Coordinator: Markus Bär (PTB)

Cardiovascular disease (CVD) is responsible for 3.9 million deaths a year in Europe. Currently, Electrocardiography (ECG) is used for a non-invasive and cost-effective way for initial clinical examinations and subsequent patient monitoring. Automated detection systems and computer-based machine learning techniques are becoming available for diagnosing and monitoring CVD such as ischemia and arrhythmia. To build trust in automated CVD diagnostics, and help reduce healthcare costs, a standardised procedure needs development to validate complex underlying algorithms and machine learning techniques.

 

This project developed a synthetic reference ECG measurement dataset, including healthy variations and selected CVD pathologies, to performance test CVD diagnostic devices. The project has, for the first time, provided traceability for CVD data analysis techniques. Such standardised testing will help manufacturers develop new ECG devices with improved CVD diagnosis reliability, thus helping promote uptake of the technology, both in clinical use and for monitoring equipment for use in the home.

 

A Framework for the Generation of Digital Twins of Cardiac Electrophysiology from Clinical 12-leads ECGs
2023

Zenodo

Automated Framework for the Inclusion of a His–Purkinje System in Cardiac Digital Twins of Ventricular Electrophysiology
2023

Zenodo

Influence of Electrode Placement on the Morphology of In Silico 12 Lead Electrocardiograms
2023

Zenodo

ECG Feature Importance Rankings: Cardiologists vs. Algorithms
2023

Arxiv

Comparison of Propagation Models and Forward Calculation Methods on Cellular, Tissue and Organ Scale Atrial Electrophysiology
2023

IEEE Transactions on Biomedical Engineering

Global Sensitivity Analysis and Uncertainty Quantification for Simulated Atrial Electrocardiograms
2022

Metrology

Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models
2022

Machine Learning for Health, Arxiv

Exploring Role of Accessory Pathway Location in Wolff-Parkinson-White Syndrome in a Model of Whole Heart Electrophysiology
2022

2022 Computing in Cardiology Conference (CinC)

Self-supervised representation learning from 12-lead ECG data
2022

Computers in Biology and Medicine

Improving Clinical ECG-based Atrial Fibrosis Quantification With Neural Networks Through in Silico P waves From an Extensive Virtual Patient Cohort
2022

2022 Computing in Cardiology Conference (CinC)

A Large-scale Virtual Patient Cohort to Study ECG Features of Interatrial Conduction Block
2022

Current Directions in Biomedical Engineering

A personalized real-time virtual model of whole heart electrophysiology
2022

Frontiers in Physiology

Sensitivity Analysis of Electrocardiogram Features to Computational Model Input Parameters
2022

Computing in Cardiology

Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram
2022

EP Europace

Multi-Class ECG Feature Importance Rankings: Cardiologists vs Algorithms
2022

Computing in Cardiology

Automated Framework for the Augmentation of Missing Anatomical Structures and Generation of Personalized Atrial Models from Clinical Data
2021

2021 Computing in Cardiology (CinC)

Sensitivity and Generalization of a Neural Network for Estimating Left Atrial Fibrotic Volume Fractions from the 12-lead ECG
2021

Current Directions in Biomedical Engineering

Robustness of convolutional neural networks to physiological electrocardiogram noise
2021

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

ECGdeli - An open source ECG delineation toolbox for MATLAB
2021

SoftwareX

Investigating the Robustness of Deep Learning to Electrocardiogram Noise
2021

Computing in Cardiology 2021

A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations
2021

Medical Image Analysis

Non-Invasive and Quantitative Estimation of Left Atrial Fibrosis Based on P Waves of the 12-Lead ECG—A Large-Scale Computational Study Covering Anatomical Variability
2021

Journal of Clinical Medicine

Computer Modeling of the Heart for ECG Interpretation—A Review
2021

Hearts

Classification of Bundle Branch Blocks with QRS Templates Extracted from 12-lead ECGs
2021

Current Directions in Biomedical Engineering

PTB-XL, a large publicly available electrocardiography dataset
2020

Scientific Data

Quantification of Interpatient 12-lead ECG Variabilities within a Healthy Cohort
2020

Current Directions in Biomedical Engineering

Quantification and classification of potassium and calcium disorders with the electrocardiogram: What do clinical studies, modeling, and reconstruction tell us?
2020

APL Bioengineering

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
2020

IEEE Journal of Biomedical and Health Informatics

Deep Learning Applied to Attractor Images Derived from ECG Signals for Detection of Genetic Mutation
2019

2019 Computing in Cardiology Conference (CinC)

PTB-XL+, a comprehensive electrocardiographic feature dataset
2013

Scientific Data

A Bi-atrial Statistical Shape Model and 100 Volumetric Anatomical Models of the Atria
0

Zenodo

Biventricular statistical shape model of the human heart adapted for computer simulations
0

Zenodo

tmehari/ptbxl_feature_benchmark: First Release
0

MedalCare-XL
0

PTB-XL, a large publicly available electrocardiography dataset
0

PhysioNet

Participating EURAMET NMIs and DIs

IMBiH (Bosnia and Herzegovina)

LNE (France)

NPL (United Kingdom)

PTB (Germany)

Other Participants

Arrhythmia Alliance (United Kingdom)
Fraunhofer-Gesellschaft zur Foerderung der angewandten Forschung e.V. (Germany)
Karlsruher Institut fuer Technologie (Germany)
King's College London (United Kingdom)
Medizinische Universität Graz (Austria)
Technische Universität Berlin (Germany)

Information

Programme
EMPIR
Field
Health
Status
completed
Call
2018
Duration
2019-2022
Project website
Link