| Abstract [eng] |
Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting over 50 million people worldwide, with its true prevalence likely being higher due to asymptomatic cases. AF places a significant burden on healthcare systems due to complications such as stroke and heart failure. Early detection is crucial but remains challenging, and current treatments primarily rely on anticoagulants and antiarrhythmic drugs, which are associated with significant side effects. Recent research highlights the role of modifiable AF triggers—acute exposures that contribute to the short-term occurrence of AF episodes (e.g., alcohol, physical exertion, stress). Identifying and managing triggers, such as alcohol consumption, physical exertion, and psychological stress, can empower patients to modify their lifestyles and align with personalized AF management strategies. This doctoral thesis addresses the clinically relevant scientific-technological challenges of detecting suspected AF triggers in physiological signals and identifying their relation to AF episode occurrence on an individual level. For trigger detection, ECG and acceleration signals have been used to compute time-varying parameters, with distinct thresholds for specific trigger identification. A quantitative approach has been proposed to assess the relational strength between suspected AF triggers and AF episode occurrence, relying on the pre- and post-trigger AF burden, defined as the percentage of time spent in AF during the monitored period. Additionally, a model for simulating trigger-affected AF episode occurrence has been developed to evaluate the proposed relation assessment methods. |
