Atrial flutter (AFl) is a common heart rhythm disor- der driven by different self-sustaining electrophysiological atrial mechanisms. In the present work, we sought to dis- criminate which mechanism is sustaining the arrhythmia in an individual patient using non-invasive 12-lead elec- trocardiogram (ECG) signals. Specifically, we analyse the influence of atrial and torso geometries for the success of such discrimination. 2,512 ECG were simulated and 151 features were extracted from the signals. Three clas- sification scenarios were investigated: random set clas- sification; leave-one-atrium-out (LOAO); and leave-one- torso-out (LOTO). A radial basis neural network classifier achieved test accuracies of 89.84%, 88.98%, and 59.82% for the random set classification, LOTO, and LOAO, re- spectively. The most discriminative single feature was the F-wave duration (74% test accuracy). Our results show that a machine learning approach can potentially identify a high number of different AFl mechanisms using the 12- lead ECG. More than the 8 atrial models used in this work should be included during training due to the significant influence that the atrial geometry has on the ECG signals and thus on the resulting classification. This non-invasive classification can help to identify the optimal ablation strategy, reducing time and resources required to conduct invasive cardiac mapping and ablation procedures.
Atrial fibrillation (AF) is an irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. In the present work, we sought to characterize and discriminate whether simulated single stable rotors are located in the pulmonary veins (PVs) or not, only by using non-invasive signals (i.e., the 12-lead ECG). Several features have been extracted from the signals, such as Hjort descriptors, recurrence quantification analysis (RQA), and principal component analysis. All the extracted features have shown significant discriminatory power, with particular emphasis to the RQA parameters. A decision tree classifier achieved 98.48% accuracy, 83.33% sensitivity, and 100% specificity on simulated data. Clinical relevance— This study might guide ablation proce- dures, suggesting doctors to proceed directly in some patients with a pulmonary veins isolation, and avoiding the prior use of an invasive atrial mapping system.
Atrial fibrillation (AF) is the most frequent irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. The non-invasive localization of AF drivers can lead to improved personalized ablation strategy, suggesting pulmonary vein (PV) isolation or more complex extra- PV ablation procedures in case the driver is on other atrial regions. We used a Machine Learning approach to characterize and discriminate simulated single stable rotors (1R) location: PVs, left atrium (LA) excluding the PVs, and right atrium (RA), utilizing solely non-invasive signals (i.e., the 12-lead ECG). 1R episodes sustaining AF were simulated. 128 features were extracted from the signals. Greedy forward algorithm was implemented to select the best feature set which was fed to a decision tree classifier with hold-out cross-validation technique. All tested features showed significant discriminatory power, especially those based on recurrence quantification analysis (up to 80.9% accuracy with single feature classification). The decision tree classifier achieved 89.4% test accuracy with 18 features on simulated data, with sensitivities of 93.0%, 82.4%, and 83.3% for RA, LA, and PV classes, respectively. Our results show that a machine learning approach can potentially identify the location of 1R sustaining AF using the 12-lead ECG.