Metrology of automated data analysis for cardiac arrhythmia management

Short Name: MedalCare, Project Number: 18HLT07
Heart monitor vector

Validating software for automatic diagnoses of cardiovascular diseases


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 will develop a synthetic reference ECG measurement dataset, including healthy variations and selected CVD pathologies, to performance test CVD diagnostic devices. The project will, for the first time, provide 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.

Other Participants
Arrhythmia Alliance (United Kingdom)
Karlsruher Institut fuer Technologie (Germany)
King's College London (United Kingdom)
Medizinische Universität Graz (Austria)
Technische Universität Berlin (Germany)