Clinically-guided data processing methods and in silico models to improve patient stratification and therapy response in cardiac and neurological disorders: PID2022-140556OB-I00

Clinically-guided data processing methods and in silico models to improve patient stratification and therapy response in cardiac and neurological disorders: PID2022-140556OB-I00

Multi-disciplinary, multi-sectoral and multi-national training network on digital biomarkers for supraventricular arrhythmia characterization and risk assessment. 101119941 (Call HORIZON-MSCA-2022-DN-01)

Multi-disciplinary, multi-sectoral and multi-national training network on digital biomarkers for supraventricular arrhythmia characterization and risk assessment. 101119941 (Call HORIZON-MSCA-2022-DN-01)

Digital transformation of clinical practice by using patient-specific cardiac digital twins for risk prediction and therapy optimization (DIGICARDIO) – TED2021-130459B-I00

Digital transformation of clinical practice by using patient-specific cardiac digital twins for risk prediction and therapy optimization (DIGICARDIO) – TED2021-130459B-I00

ECG morphology clusters for sudden cardiac death risk stratification in coronary artery disease using genetics and human-based computational models (STRATEGIC) – PID2021-128972OA-I00

ECG morphology clusters for sudden cardiac death risk stratification in coronary artery disease using genetics and human-based computational models (STRATEGIC) – PID2021-128972OA-I00

Consulting services for basic physiologic features

Consulting services for basic physiologic features

Acabados

Scientific Network Observatory for the Study and Prevention of Sudden Death in Sport (PREMUBID)

Biomarkers for Early Detection of Atrial Fibrillation Phase in Strenuous Exercise-Induced Atrial Cardiomyopathy: Prevention of Pafiyama Syndrome

PIC: Personalised in Silico Cardiology

MY-ATRIA: MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression

Detection of K+ concentration alterations from the ECG

Validation of pulse rate variability, respiratory frequency and stress assessment from NIVAband

WECARMON: Wearable Cardiorespiratory Monitor

Multi-Scale Physiology-Driven Computational Tools To Assist In The Assessment And Management Of Cardiac Dysfunctions (MScPhCarDys)

Biomedical Engineering and Medical Physics (BioMep)

Multiscale computational tools to improve diagnosis, risk assessment and treatment in prevalent heart diseases (MULTITOOLS2HEART)

Multimodal analysis and m-Health tools for diagnostic and monitoring improving of Obstructive Lung Disease and Obstructive Sleep Apnea patients (M-LODOSA)

ERC Starting Grant. Is your heart aging well? A systems biology approach to characterize cardiac aging from the cell to the body surface (MODELAGE)

The Autonomous Nervous System as a modulator of cardiac function: comprehensive investigation through signal processing and computational modeling

Physiology-guided signal processing for cardiac risk stratification and therapy-guided: tools to improve decision-making in cardiac management

Computer-assisted interpretation of electrical signals: a step forward in understanding and treating cardiac diseases (INTERCARDIO)

QT-bed: Evaluation of changes in ventricular repolarization and its relationship with heart rate during bed-rest experiments

Computational investigation of cardiac arrhythmias and drug effect: from ion channels to the surface electrocardiogram

Multimodal biomedical signal processing for the analysis and characterization of cardiovascular, respiratory and autonomic deseases

Towards improved management of cardiovascular diseases by integrative in silico-in vitro-in vivo research into hearts structure, function and autonomic regulation – PID2019-105674RB-I00

Physiology-driven signal analysis for guiding cardiac arrhythmia management and therapy – PID2019-104881RB-I00

Physical activity for the prediction and prevention of cardiac risk: EXP_75053

Efficacy, safety and mechanisms of new techniques for cardiac stimulation in patients with pacemakers: from computational research to clinical practice – LMP94_21