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In-Silico Data Based Machine Learning Technique Predicts Premature Ventricular Contraction Origin Coordinates
Andony Arrieula
,
Hubert Cochet
,
Pierre Jaïs
,
Michel Haïssaguerre
,
Nejib Zemzemi
,
et al.
Computing in Cardiology 2021 - 48th Computing in Cardiology Conference, Sep 2021, Brno / Virtual, Czech Republic
Conference papers
hal-03364532v1
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A Patient-Specific Equivalent Dipole Model
Gabriel Cardoso
,
Geneviève Robin
,
Andony Arrieula
,
Mark Potse
,
Michel Haïssaguerre
,
et al.
Computing in Cardiology 2022, Sep 2022, Tampere, Finland
Conference papers
hal-03936912v1
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In-Silico Evaluation of an Iterative Pace-Mapping Technique to Guide Catheter Ablation of Ventricular Ectopy
Andony Arrieula
,
Hubert Cochet
,
Pierre Jaïs
,
Michel Haïssaguerre
,
Mark Potse
Conference papers
hal-02299186v4
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Prédiction de l'affluence journalière dans un réseau de transports urbains
Apolline Louvet
,
Andony Arrieula
,
Jean Prost
,
Paul Freulon
2020
Preprints, Working Papers, ...
hal-03043105v1
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An Improved Iterative Pace-Mapping Algorithm to Detect the Origin of Premature Ventricular Contractions
Andony Arrieula
,
Hubert Cochet
,
Pierre Jaïs
,
Michel Haïssaguerre
,
Mark Potse
Conference papers
hal-02944731v5
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Méthodes numériques d'apprentissage pour faciliter la localisation des arythmies ventriculaires lors d'une procédure d'ablation
Andony Arrieula
Theses
tel-03875870v1
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Deep Learning for Model Correction in Cardiac Electrophysiological Imaging
Victoriya Kashtanova
,
Ibrahim Ayed
,
Andony Arrieula
,
Mark Potse
,
Patrick Gallinari
,
et al.
MIDL 2022 - Medical Imaging with Deep Learning, Jul 2022, Zurich, Switzerland
Conference papers
hal-03687596v1
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