Key result
Novel deep learning approach achieves ~96% accuracy for cardiac arrhythmia detection.
Why the study?
Automated ECG classification for diagnosing cardiovascular diseases remains challenging due to inter-patient variability, signal noise, and heart rhythm complexity.
Does a combined TCN and GCN deep learning approach improve classification accuracy for cardiac arrhythmia detection in ECG signals compared to standard deep learning architectures?
Does a combined TCN and GCN deep learning approach improve classification accuracy for cardiac arrhythmia detection in ECG signals compared to standard deep learning architectures?
A novel deep learning approach combining TCN and GCN achieves 95.5% accuracy in detecting cardiac arrhythmias from ECG signals, outperforming standard graph-based methods.
Electrocardiogram (ECG) signals play a critical role in diagnosing cardiovascular diseases (CVDs), yet automated ECG classification remains challenging due to inter-patient variability, signal noise, and heart rhythm complexity. To address these challenges, we propose a novel deep learning approach that combines Temporal Convolutional Networks (TCN) with Graph Convolutional Networks (GCN) for robust cardiac arrhythmia detection. Unlike conventional methods, our approach constructs cycle graphs from ECG signals, leveraging graph signal processing to enhance data representation and improve classification accuracy. The proposed model processes raw ECG signals using a TCN-based feature extractor, followed by spectral graph convolutions via a GCN. We evaluate our model using the Chapman and Shaoxing 12-lead ECG database, consolidating 11 rhythm classes into four superclasses for improved classification reliability. Our model achieves a high classification accuracy of 95.5%, surpassing standard GCNs and other deep learning architectures while significantly reducing graph construction complexity. These results demonstrate the potential of graph-based deep learning for efficient and accurate ECG analysis in resource-constrained medical settings. • Novel graph construction approach for ECG signal analysis. • Enhanced temporal feature extraction using a Temporal Convolutional Network. • Graph learning techniques refine TCNN for improved signal processing. • Outperforms standard graph-based approaches for arrhythmia detection. • Achieves performance comparable to state-of-the-art ECG classification methods.
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Yaser et al. (2025) studied Cardiac arrhythmias. Temporal Convolutional Networks (TCN) combined with Graph Convolutional Networks (GCN) vs. Standard GCNs and other deep learning architectures was evaluated on Classification accuracy. A novel deep learning approach combining Temporal Convolutional Networks and Graph Convolutional Networks achieved a high classification accuracy of 95.5% for cardiac arrhythmia detection.
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