Cardiac Arrhythmia Disorders Detection with Deep Learning Models

In this paper, the research of computer algorithms for automatic detection of heart rhythm disorders based on the analysis of electrocardiograms have been conducted. A new model of the electrocardiogram classifier is proposed, as an ensemble of a two-dimensional convolutional neural network and a long short-term memory model, which includes an attention layer. Computer experiments conducted on the MIT-BIH Physionet collection of ECG showed its high performance compared to other models of machine and deep learning. Proposed model successfully detected cardiac arrhythmia classes with an accuracy of 99.34%, AUC = 99% and recall = 99%. © 2022, Springer Nature Switzerland AG.

Authors
Shchetinin E.Y.1 , Sevastianov L.A. 2, 3 , Demidova A.V. 3 , Glushkova A.G.4
Publisher
Springer Science and Business Media Deutschland GmbH
Language
English
Pages
371-384
Status
Published
Volume
1552 CCIS
Year
2022
Organizations
  • 1 Financial University, Government of the Russian Federation 49, Leningradsky Prospect, Moscow, 125993, Russian Federation
  • 2 Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, Moscow, 117198, Russian Federation
  • 3 Joint Institute for Nuclear Research 6, Joliot-Curie Street, Moscow region, Dubna, 141980, Russian Federation
  • 4 Oxford University, Oxford, United Kingdom
Keywords
Attention module; Cardiac arryhythmia; Cardiovascular diseases; Convolutional neural network; ECG; Long short-term memory; MIT-BIH
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