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Titre : Interpretability of Neural Networks applied to Electrocardiograms: Translational Applications in Cardiovascular Diseases
Date : 18 septembre 2023
Heure: 10:00
Résumé : Electrocardiograms (ECGs) are non-invasive tools for assessing the electrical activity of the heart, they are widely used to detect cardiac abnormalities. Deep learning algorithms enable automatic detection of complex patterns in ECG data, offering significant potential for improved cardiac diagnosis. However, their adoption is hindered by a low level of trust among medical professionals and a substantial need for data to train the models. Artificial intelligence, particularly deep learning, allows for exploration of hierarchical representations of complex data, leading to a better understanding of internal interactions. Nevertheless, interpretability of the models are crucial to gain specialists' trust and facilitate widespread implementation. This thesis aims to develop a novel interpretability algorithm for neural networks applied to ECG analysis, working in close collaboration with cardiology specialists. Our study focuses on a specific cardiac pathology, Torsades-de-Pointes (TdP).
Lieu : Faculté de Médecine Sorbonne Université, 105, boulevard de l’Hôpital. Salle Bordeaux (bâtiment 105)