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CNN-GRU model for ECG signal classification using UCR time series data
- 1 Hong Kong University
* Author to whom correspondence should be addressed.
Abstract
This study presents a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model designed to classify electrocardiogram (ECG) signals into five categories, including normal and abnormal heart activities. The model leverages CNN layers to extract critical local features from ECG signals, while GRU layers capture temporal dependencies in heartbeat sequences. Squeeze-and-Excitation (SE) blocks were incorporated to enhance the model's focus on important signal components. Using the ECG5000 dataset from the UCR Time Series Classification Archive, the model was trained with data augmentation techniques such as time scaling, time shifting, and noise addition to improve its robustness. After training for 20 epochs with an Adam optimizer, the model achieved a test accuracy of 94.19%, demonstrating its effectiveness in distinguishing between different heart conditions. This automated classification system holds significant potential for aiding healthcare professionals in diagnosing heart diseases more accurately and efficiently, offering critical support in clinical decision-making.
Keywords
ECG classification, CNN-GRU model, deep learning, heart disease detection, time-series data
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Cite this article
Xu,C. (2024). CNN-GRU model for ECG signal classification using UCR time series data. Advances in Engineering Innovation,12,31-35.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Journal：Advances in Engineering Innovation
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