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Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG.
Luongo, Giorgio; Azzolin, Luca; Schuler, Steffen; Rivolta, Massimo W; Almeida, Tiago P; Martínez, Juan P; Soriano, Diogo C; Luik, Armin; Müller-Edenborn, Björn; Jadidi, Amir; Dössel, Olaf; Sassi, Roberto; Laguna, Pablo; Loewe, Axel.
Afiliación
  • Luongo G; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany.
  • Azzolin L; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany.
  • Schuler S; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany.
  • Rivolta MW; Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy.
  • Almeida TP; Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom.
  • Martínez JP; I3A, Universidad de Zaragoza, and CIBER-BNN, Zaragoza, Spain.
  • Soriano DC; Engineering, Modelling and Applied Social Sciences Centre, ABC Federal University, São Bernardo do Campo, Brazil.
  • Luik A; Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany.
  • Müller-Edenborn B; Department of Electrophysiology, University-Heart-Center Freiburg-Bad Krozingen, Bad Krozingen Campus, Bad Krozingen, Germany.
  • Jadidi A; Department of Electrophysiology, University-Heart-Center Freiburg-Bad Krozingen, Bad Krozingen Campus, Bad Krozingen, Germany.
  • Dössel O; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany.
  • Sassi R; Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy.
  • Laguna P; I3A, Universidad de Zaragoza, and CIBER-BNN, Zaragoza, Spain.
  • Loewe A; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Cardiovasc Digit Health J ; 2(2): 126-136, 2021 Apr.
Article en En | MEDLINE | ID: mdl-33899043
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cardiovasc Digit Health J Año: 2021 Tipo del documento: Article País de afiliación: Alemania
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cardiovasc Digit Health J Año: 2021 Tipo del documento: Article País de afiliación: Alemania