IDOVEN 1903 S.L. is a Spanish company that has developed WILLEM, a cloud-based Artificial Intelligence (AI) software that analyses electrocardiograms (ECG) to diagnose cardiac arrhythmia. Currently, IDOVEN offers three cardiological services: (1) ECG reports for citizens, in which a ECG monitoring kit is sent anywhere in Europe for people who want to check their heart activity; (2) ECG analysis of long-term Holters for hospitals; and (3) ECG monitoring for patients at home to alert hospitals when patients suffer from relevant cardiac episodes.

In the H2020 MAESTRIA, IDOVEN can contribute with its experience in high-performance digital ECG analysis to provide an automatic remote service for the scientific developments of the project using cutting-edge AI models and statistical methods. Specifically, IDOVEN can focus its expertise in two fields:

  1. Differentiating electrical AF patterns between patients suffering from different cardiopathies to stratify the cardiac risk and to know the atrial remodelling condition from the electrical activity. This differentiation was demonstrated by CNIC and investigators of IDOVEN for ventricular fibrillatory patterns (J.M. Lillo-Castellano et al. Safety threshold of R-wave amplitudes in patients with implantable cardioverter defibrillator. Heart. 2016;102(20):1662-70.). The work hypothesis is that similar mechanisms as the ones they observed in the ventricles during spontaneous ventricular fibrillation, can occur in the atrium during atrial fibrillation. Where different substrates, baseline cardiomyopathies and states of the atrium remodeling could have different fibrillatory patterns and electrical behaviour.
  2. Predicting the progression of AF remodeling at 6 months – 1-year follow-up, from ECG raw data patterns with a similar methodology and technology as the recently reported in patients with implantable devices by CNIC and IDOVEN investigators (J.M. Lillo-Castellano et al. Personalized Monitoring of Electrical Remodelling during Atrial Fibrillation Progression via Remote Transmissions from Implantable Devices, Europace, December 2019. [Epub ahead of print].). Results in this work enabled us to develop new software tools to detect individual- specific parameters that allow us to estimate the progression of AF remodeling at 6 months (based on electrophysiological patterns). Then, this expertise can be extrapolated to non-invasive long-term recording devices such as Holter ECG as we do at IDOVEN during continuous ECG recording of one month.