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[2602.17701] Deep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation
Summary
This article evaluates various deep neural network architectures for ECG classification, highlighting the effectiveness of CNN-LSTM models and ensemble strategies in improving arrhythmia detection.
Why It Matters
With cardiovascular diseases on the rise, accurate and automated ECG classification is crucial for timely diagnosis. This study provides insights into advanced neural network techniques that enhance detection reliability and interpretability, addressing a significant healthcare need.
Key Takeaways
- CNN-LSTM models provide a strong balance between sensitivity and specificity for ECG classification.
- Dynamic ensemble strategies can significantly improve performance in class-imbalanced datasets.
- The study utilized data augmentation techniques to enhance minority class representation in ECG datasets.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.17701 (eess) [Submitted on 7 Feb 2026] Title:Deep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation Authors:Yun Song, Wenjia Zheng, Tiedan Chen, Ziyu Wang, Jiazhao Shi, Yisong Chen View a PDF of the paper titled Deep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation, by Yun Song and 5 other authors View PDF HTML (experimental) Abstract:With the rising prevalence of cardiovascular diseases, electrocardiograms (ECG) remain essential for the non-invasive detection of cardiac abnormalities. This study presents a comprehensive evaluation of deep neural network architectures for automated arrhythmia classification, integrating temporal modeling, attention mechanisms, and ensemble strategies. To address data scarcity in minority classes, the MIT-BIH Arrhythmia dataset was augmented using a Generative Adversarial Network (GAN). We developed and compared four distinct architectures, including Convolutional Neural Networks (CNN), CNN combined with Long Short-Term Memory (CNN-LSTM), CNN-LSTM with Attention, and 1D Residual Networks (ResNet-1D), to capture both local morphological features and long-term temporal dependencies. Performance was rigorously evaluated using accuracy, F1-score, and Area Under the Curve (AUC) with 95\% confidence intervals to ensure statistical robustness, while Gradient-weighted Class Activation Map...
