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.

Machine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation from the ECG / G. Luongo, L. Azzolin, M.W. Rivolta, T.P. Almeida, J.P. Martinez, D.C. Soriano, O. Dossel, R. Sassi, P. Laguna, A. Loewe - In: 2020 Computing in Cardiology[s.l] : IEEE Computer Society, 2020. - ISBN 9781728173825. - pp. 1-4 (( Intervento presentato al 47. convegno CinC tenutosi a Rimini nel 2020 [10.22489/CinC.2020.181].

Machine Learning to Find Areas of Rotors Sustaining Atrial Fibrillation from the ECG

M.W. Rivolta;R. Sassi;
2020

Abstract

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.
Settore INF/01 - Informatica
Settore ING-INF/06 - Bioingegneria Elettronica e Informatica
H20MCITNIF17RSASS_01 - MutlidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression - SASSI, ROBERTO - H20MCITNIF - Horizon 2020_Marie Skłodowska-Curie actions-Innovative Training Network (ITN)/Individual Fellowships (IF) - 2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/824351
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