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.
Our hypothesis is that autonomic nervous system (ANS) induced modulation in cardiac activity can be used as a diagnostic biomarker of arrhythmia progression. Although AF has received much attention in the scientific community, there is still a complete lack of tools for analysis of ANS modulation in AF. Developing such tools is challenging, since ANS induced modulation in AF results from complex interactions between the ANS, the atria, and the AV node.In this project, we will combine signal processing, cardiac computational modeling and machine learning to develop optimized tools for non-invasive assessment cardiac ANS induced modulation in AF. Specifically, we will develop 1) diagnostic biomarkers of ANS induced modulation in the atria that can be assessed from the resting ECG and 2) diagnostic biomarkers of ANS induced modulation in the heart rate that can be assessed from e.g. smartwatches. Within the project we will also evaluate the feasibility of using the diagnostic biomarkers for clinical decision support.The project will span over 4 years and the research will be carried out by a PhD student with supervision from senior researchers with expertise in biomedical signal processing, computational cardiac modelling and clinical AF research, respectively.