Recurrent Neural Network-based Model for Electrocardiogram Classification
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Abstract
This paper presents the study of deep learning models for Electrocardiogram (ECG) classification. Abnormalities of heart diseases can detect and diagnose by ECG signal. Deep learning models have been interested for arrhythmia detection and classification from ECG signal. Recurrent Neural Networks (RNNs) represent a different category of artificial neural networks that incorporate feedback mechanisms. It makes suitable for capturing temporal dependencies in the time series data from ECG electrical signal. Long short-term memory (LSTM) network and gated recurrent unit (GRU) are types of RNN designs. The objective of this paper is to compare the performance of conventional RNN with LSTM and GRU models for capturing the long-term relationships in ECG data. Simulation results show that GRU model can classify the heart’s diseases more accuracy than simple RNN and LSTM model.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright @2021 Engineering Transactions
Faculty of Engineering and Technology
Mahanakorn University of Technology
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