Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram. Issue 7 (19th January 2022)

• Record Type: Journal Article
• Authors: Luongo, Giorgio; Vacanti, Gaetano; Nitzke, Vincent; Nairn, Deborah; Nagel, Claudia; Kabiri, Diba; Almeida, Tiago P; Soriano, Diogo C; Rivolta, Massimo W; Ng, Ghulam André; Dössel, Olaf; Luik, Armin; Sassi, Roberto; Schmitt, Claus; Loewe, Axel
• Is Part Of: Europace. Volume 24:Issue 7(2022)
• Page Start: 1186
• Page End: 1194
• Publication Date: 2022-01-19
• Subjects: Atrial flutter -- Electrocardiography -- Machine learning -- Cardiac modelling -- Personalized medicine; Arrhythmia -- Treatment -- Periodicals; Cardiac pacing -- Periodicals; Catheter ablation -- Periodicals; Heart -- Physiology -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1093/europace/euab322
• Languages: English
• ISSNs: 1099-5129

Abstract

Abstract: Aims: Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). Methods and results: Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. Conclusion: Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments. Graphical Abstract: