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Spatio-Temporal ECG Network for Detecting Cardiac Disorders from Multi-Lead ECGs: 2021 Computing in Cardiology, CinC 2021

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Published
  • L. Chen
  • Z. Jiang
  • T.P. Almeida
  • F.S. Schlindwein
  • J.S. Shoker
  • G.A. Ng
  • H. Zhou
  • X. Li
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Publication date10/01/2022
Host publication 2021 Computing in Cardiology (CinC)
PublisherIEEE
Pages1-4
Number of pages4
ISBN (Electronic)9781665479165
ISBN (Print)9781665467216
<mark>Original language</mark>English
Event2021 Computing in Cardiology (CinC) - Brno, Czech Republic
Duration: 13/09/202115/09/2021
http://www.cinc2021.org/about-the-conference/

Conference

Conference2021 Computing in Cardiology (CinC)
Country/TerritoryCzech Republic
CityBrno
Period13/09/2115/09/21
Internet address

Conference

Conference2021 Computing in Cardiology (CinC)
Country/TerritoryCzech Republic
CityBrno
Period13/09/2115/09/21
Internet address

Abstract

Automatic detection and classification of cardiac disorders play a critical role in the analysis of clinical electrocardiogram (ECG). Deep learning methods are effective for automated feature extraction and have shown promising results in ECG classification. In this work, we proposed a deep spatio-temporal ECG network (ST-ECGNet) to extract robust spatio-temporal features for detecting multiple cardiac disorders from the multi-lead ECG data. The proposed ST-ECGNet combines a Convolutional Neural Network (CNN) module for extracting local spatial features, an attention module for capturing global spatial features, and a Bi-directional Gated Recurrent Unit (Bi-GRU) module for extracting temporal features from ECG data. Specifically, the attention mechanism enables our deep learning architecture to focus on the most important and useful parts of the input to make more accurate predictions. In PhysioNet/Computing in Cardiology Challenge 2021, our entry was not officially ranked and scored on the test data of the Challenge, because our code was not successfully processed during the official phase and failed to run with errors. © 2021 Creative Commons.