Automated Diagnosis of Heart Arrhythmia Using Recurrent Neural Network

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Published: May 29, 2023
Keywords:
Cardiac Arrhythmia Recurrent Neural Network Algorithms

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K. Jummelal
Assistant Professor, Department of CSE, Sridevi Women’s Engineering College, Hyderabad, Telangana
M.Sai Prathyusha
UG Student, Department of CSE, Sridevi Women’s Engineering College, Hyderabad, Telangana
U. Monika Sai
UG Student, Department of CSE, Sridevi Women’s Engineering College, Hyderabad, Telangana
A. Sravanthi
UG Student, Department of CSE, Sridevi Women’s Engineering College, Hyderabad, Telangana

Abstract

The term "cardiac arrhythmia" refers to irregular heartbeats. This study's major goal is to use deep learning algorithms to detect cardiac arrhythmias from ECG signals with the least amount of data pre-processing necessary. To automatically detect irregularities, our method combines recurrent structures with CNN, such as recurrent neural networks (RNN), long short-term memories (LSTM), gated recurrent units (GRU), and a mixture of CNN and recurrent structures. Contrary to traditional analysis approaches, deep learning algorithms do not rely on feature extraction-based analysis techniques. All tests are executed for 1000 epochs within a defined range of learning rates to ascertain the best parameters for the deep learning approaches.

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How to Cite
K. Jummelal, M.Sai Prathyusha, U. Monika Sai, & A. Sravanthi. (2023). Automated Diagnosis of Heart Arrhythmia Using Recurrent Neural Network. Journal of Coastal Life Medicine, 11(2), 681–685. Retrieved from https://www.jclmm.com/index.php/journal/article/view/1066
Issue
Vol. 11: Number 2, 2023
Section
Articles

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