My research group develops digital and data-driven technologies for improved diagnosis, patient monitoring, and personalized care in cardiovascular disease. Our research is driven by clinical needs and focuses on developing computational methods that transform physiological signals and health data into clinically meaningful decision-support tools. We develop hybrid methods that combine biomedical signal processing, physiologically informed mathematical modelling, and artificial intelligence with clinical expertise to bridge the gap between engineering innovation and patient care.

A major focus of our research is atrial fibrillation, where we develop novel methods for ECG-based characterization of cardiac electrophysiology and autonomic regulation. By combining physiological modelling with machine learning, we derive digital biomarkers and physiologically meaningful parameters that improve the assessment of disease mechanisms, treatment response, and disease progression. We develop parsimonious physiologically informed models that can be fitted to clinical data, enabling robust estimation of physiologically meaningful parameters from routinely acquired cardiovascular signals. Our research spans methodological development, validation in clinical studies, and translation into clinical practice through close collaborations with clinicians, healthcare providers, and industry.

Frida Sandberg is Professor of Biomedical Engineering with specialization in Digital and Data-Driven Health at Lund University. She is Coordinator of the LTH Profile Area Engineering Health and serves as Programme Director for the Biomedical Engineering programmes at Lund University. She received the M.Sc. degree in Electrical Engineering in 2003 and the Ph.D. degree in Signal Processing in 2010, both from Lund University. Her research bridges engineering, data science, and clinical medicine to develop innovative technologies for personalized cardiovascular care.