Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. To cut costs and reduce patient suffering, tools that help clinicians to optimize personalized AF treatment are needed. The purpose of the project is to develop a novel methodology to quantify the individual cardiac autonomic nervous system (ANS) response from ECG during AF. The autonomic nervous system plays an important role in initiation and maintenance of AF, but its exact role is thought to differ significantly between patients. Thus, information about patient specific cardiac ANS response may be a key factor in personalized AF management. However, there are currently no tools available in the clinic for quantitative analysis of cardiac ANS response during AF. Developing such tools is challenging, since ANS induced changes during AF are very subtle and result from complex interactions between the atria and the atrioventricular (AV) node. In this project we aim to develop a methodology able to extract this information, using a mathematical modeling approach that combine detailed biophysical modeling with advanced signal processing and statistical methods. The methodology will be used to extract information about how the ANS affects the electrical activation of the atria, the ventricles and AV node, which will be correlated with patient data on AF progression to assess its diagnostic power. Thus, the project has the potential to create new clinical tools which will improve patient stratification and treatment optimization for this large and diverse group of patients.