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Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks

Meryll Dindin 1 Yuhei Umeda 2 Frédéric Chazal 3 
3 DATASHAPE - Understanding the Shape of Data
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Saclay - Ile de France
Abstract : This paper presents an innovative and generic deep learning approach to monitor heart conditions from ECG signals.We focus our attention on both the detection and classification of abnormal heartbeats, known as arrhythmia. We strongly insist on generalization throughout the construction of a deep-learning model that turns out to be effective for new unseen patient. The novelty of our approach relies on the use of topological data analysis as basis of our multichannel architecture, to diminish the bias due to individual differences. We show that our structure reaches the performances of the state-of-the-art methods regarding arrhythmia detection and classification.
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Contributor : Frédéric Chazal Connect in order to contact the contributor
Submitted on : Friday, June 14, 2019 - 7:38:51 AM
Last modification on : Tuesday, July 5, 2022 - 8:38:24 AM


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  • HAL Id : hal-02155849, version 1
  • ARXIV : 1906.05795


Meryll Dindin, Yuhei Umeda, Frédéric Chazal. Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks. CanadianAI 2020 - 33rd Canadian Conference on Artificial Intelligence, May 2020, Ottawa, Canada. ⟨hal-02155849⟩



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