PTB has established an ECG-database as part of PhysioNet and has carried out work in computational modelling of electrophysiology and statistical data analysis. PTB has coordinated the EMRP project NEW04 “Novel mathematical and statistical methods for uncertainty evaluation” and will also coordinate this project. PTB currently chairs the European Centre for Mathematics and Statistics in Metrology (MATHMET). Key topics include statistical data analysis, signal processing, and numerical modelling uncertainty quantification in complex models.

IMBiH has significant experience in international projects and management of working groups (international projects, grants, research, bilateral projects). IMBiH brings in expertise on uncertainty evaluation, using statistical methods, such as Monte Carlo methods and Bayesian approaches, and its implementation in software. IMBiH is member of the European Centre for Mathematics and Statistics in Metrology (MATHMET) for developing standardised approaches in data analysis.

LNE has a mathematics and statistics department which mainly concerned with methods for uncertainty evaluation, statistical inverse problems, and deconvolution methods in a probabilistic framework or treatment of inter-laboratory studies. Jointly with NPL and PTB, the department at LNE led a successful EURAMET inter‑disciplinary research project on uncertainty analysis applied to dynamic measurement problems.

NPL has a group dedicated to data science which consists of 18 permanent members of staff, 2 Joint Appointments (with the Universities of Surrey and Cambridge) and 12 PhD students, specialising in mathematical modelling, simulation of measuring systems, data analysis and uncertainty evaluation and takes a leading role in the Joint Committee for Guides in Metrology (JCGM) that is responsible for publishing the Guide to the expression of uncertainty in measurement (GUM).  

A-A is a coalition of charities, patient groups, patients, carers and medical experts. These independent groups work together under the Arrhythmia Alliance umbrella to promote timely and effective diagnosis and treatment of arrhythmias. The core aim of A-A is to raise awareness of cardiac arrhythmias by bringing together patients member charities, healthcare professionals, commissioners and their allies. A-A assess and quantify unmet need amongst those affected by arrhythmias. A-A will provide a platform to integrate both the patient view and the medical experts for guidance of the project.

KCL has a track record in the management of patients with complex cardiac rhythm disturbances of atrial and ventricular origin. An essential part of this collaboration is the clinical cardiac electrophysiology programme at St. Thomas’ Hospital, which is one of the largest in the UK with strong academic interest. The Clinical Arrhythmia Research Group seeks to understand better the mechanisms which cause cardiac arrhythmias in humans, atrial fibrillation and tachycardia. The group will contribute with clinical expertise in assessing new technical developments and benchmarking ML-classification in comparison to clinical experts.

KIT has an internationally renowned position in modelling of the human heart, clinical applications of computer modelling, biosignal analysis of ECG and intracardiac Electrograms and the inverse problem of ECG. The group has on-going collaborations with several international research centres.

MUG's Computational Cardiology lab provides cutting edge technologies for performing biophysically detailed simulations of atrial and ventricular depolarisation and repolarisation sequences which drive the genesis of electrical signals recorded from the body surface. The underlying software stack is being used by leading cardiac modelling labs in France, the US and also by leading device companies. The MUG specialises in automated workflows for building anatomically accurate heart-torso models from tomographic imaging data and the personalisation of these models based on clinical standard ECGs and body surface potential maps (BSPM). The forward ECG modelling workflows have been optimised for largescale statistical simulations in cloud-based environments, allowing the fine-grained exploration of parameter spaces with thousands to millions of simulations that mechanistically link electrical activity in the heart to body surface potential maps. MUG will also provide clinical expertise for the initial evaluation of the simulated ECG signals.

TUB is known for leading research in statistical learning theory for neural networks and Support Vector Machines (e.g. Kernel PCA, One-Class SVM etc.). The group has developed new approaches for the analysis of neural networks, such as Layerwise Relevance Propagation. It has widely contributed to the field of biomedical signal processing specifically working on time-series analysis, statistical denoising methods and blind source separation. The interests span a large range from the neurosciences, brain computer interfacing, computational chemistry up to genomic data analysis.