V. Nitzke. Non-invasive atrial rotors and focal sources characterisation using the 12-lead ECG: a computational study. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT). Bachelorarbeit. 2020
Atrial Fibrillation (AF) is the most common cardiac arrhythmia in clinical practice and one of the leading causes for hospitalization and death. Rotational activities (rotors) and focal activities (focal sources) are being considered to drive and sustain AF. Identifying the presence of these mechanisms is fundamental for planning the best treatment to heal patients. Commonly AF is treated through ablation of the affected area guided by intracardiac catheters. By using a non-invasive method like the Electrocardiogram (ECG), characterization of the mechanism would be simplified and the identification process would be optimized. The ECG is widely used in clinical practice and it is therefore a perfect tool for early diagnosis and arrangement of targeted treatment. Using the ECG to determine the AF mechanism would enable physicians to directly chose the right approach for therapy and further examination. This project analyzed 12-lead ECG signals obtained by simulating rotors and focal sources driving AF on a computer model of the atria. These two mechanisms were simulated in the exact same position and with the same basic cycle length (bcl) making the results comparable and ensuring that the differences in the signals arise solely from the different mechanisms. In total 31 atrial scenarios were simulated each with a unique point of rotor stimulation and bcl. Each scenario was transformed on 8 different torso geometries resulting in a total number of 248 torso scenarios. For each of the torso scenarios the 12-lead ECG was obtained and analyzed with several signal processing methods resulting in 182 features. With alpha = 0.01, 77.47% of the features used in this work showed statistically significant differences between focal source and rotors. Furthermore, the features were analyzed using the Receiver Operator Curve (ROC), and a decision tree classifier was implemented for a multi-feature classification. These methods characterized features that perform especially well in the discrimination between rotors and focal sources. Deriving from the ROC analysis the most powerful features corresponding to AUC values between 0.9297 and 0.9111 were standard deviation of voltage amplitude of the vector combination of I, aVF, and V1, the mean organization index over 12 leads, and the standard deviation of R-value from PC4. Decision tree classification achieved 89.92% average accuracy on a pre-fixed test set over 100 trials. The methods and features presented in this thesis can potentially be used to determine the mechanism driving AF. This work demonstrated that the introduced methods can reliably distinguish the most simple cases of AF mechanisms. Based on these findings it might be possible to non-invasively characterize the mechanism of AF in patients and thus will support the physicians treating this arrhythmia.