Data Science and Intelligent Computing Techniques
A Comprehensive Review on Arrhythmia Classification using Deep Learning Methods
Authors: Deepti Sharma and Narendra Kohli
Publishing Date: 13-01-2023
ISBN: 978-81-955020-2-8
Abstract
National Conference for doctors of HAL on May 2022 published in its report that, India will record the maximum number of deaths due to cardiovascular diseases (CVDs) by 2030 in the world. So its timely detection and cure can save many lives. To detect and classify CVDs, deep learning (DL) methods are widely used. This survey focuses on a variety of DL models applied in various research papers, to find models with higher accuracy in the classification of arrhythmia and other heart-related issues. Here six DL models have been focused namely Convolution Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Multi-Layer Perceptron (MLP), Deep Belief Network (DBN), and Recurrent Neural Network (RNN) after the review of 23 papers from the year 2018-2022, to find the suitable model for classification task and feature extraction where Electrocardiogram (ECG) is a common input to the every DL model.
Keywords
Arrhythmia classification, Deep Learning methods, cardiovascular heart diseases, biomedical signal processing
Cite as
Deepti Sharma and Narendra Kohli, "A Comprehensive Review on Arrhythmia Classification using Deep Learning Methods ", In: Satyasai Jagannath Nanda and Rajendra Prasad Yadav (eds), Data Science and Intelligent Computing Techniques, SCRS, India, 2023, pp. 167-183. https://doi.org/10.56155/978-81-955020-2-8-15