Abstract:

Atrial fibrillation (AFib) is one of the most common cardiac arrhythmias. Even though AFib is not a direct cause of death, it can cause fatal consequences, such as stroke or heart attack, and it affects the patient’s quality of life considerably. Two mechanisms identified as atrial fibrillation drivers that will be studied in this work are focal and rotational activities in the atria. Currently, AFib is treated through ablation of the affected tissue. This process often involves complex procedures to locate the cause for AF. Localizing and characterizing the driving mechanism of AFib beforehand could help physicians to accelerate the catheter ablation, thus reducing both costs and the risk for complications during the process. This project analyzed 12-lead ECG signals obtained by simulating rotors and focal sources driving AFib on a computer model of the atria. We started off with 2 atria models and 8 torso geometries to simulate AFib drivers. Throughout the analysis process, we utilized several signal processing methods, a greedy-forward feature selection, a randomized cross-validation technique and a decision tree as well as a neural network as classifiers. For the localization, we focused on three areas of the atrium, the pulmonary vein (PV), the non-PV areas of the left atrium (LA) and the right atrium (RA). Our final, most important classification was a hierarchical classifier, considering both drivers and all three locations, averaged at 57% accuracy. It proved particularly strong in discerning rotational activities in the PV. The findings showed that rotors and focal sources can be distinguished precisely, rotors can be located fairly well, whereas focal sources prove more complicated to localize when using a machine learning approach on associated 12-lead electrocardiogram (ECG) signals. The features and methods presented in this work can be used to ultimately help physicians classify a given AFib case in clinical practice with little risk and time.