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Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion
Chuanjiang Wang1, Junhao Ma1, Guohui Wei2
1College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China.
Insights
This study enhances cardiac arrhythmia classification using advanced Electrocardiogram (ECG) signal processing. A novel dual-channel feature fusion strategy achieves 97.80% accuracy, improving cardiovascular disease management.
Area of Science:
- Cardiology and Biomedical Engineering
- Artificial Intelligence in Healthcare
- Signal Processing
Background:
- Cardiovascular disease (CVD) is a major global health concern, with cardiac arrhythmia being a common complication.
- Accurate arrhythmia classification is crucial for effective CVD management and patient outcomes.
- Current ECG analysis methods face challenges in precision and objectivity.
Purpose of the Study:
- To introduce an innovative dual-channel feature fusion strategy for enhanced Electrocardiogram (ECG) signal processing.
- To improve the accuracy and objectivity of cardiac arrhythmia classification.
- To advance the clinical application of ECG analysis for better patient care.
Main Methods:
- Utilized Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet thresholding for noise reduction.
- Employed a ResNet-ICBAM model for feature extraction in the primary channel.
- Transformed 1D ECG signals into Gram angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP) for 2D-CNN feature extraction in the secondary channel.
- Fused features from both channels for final classification.
Main Results:
- Achieved a classification accuracy of 97.80% on the MIT-BIH arrhythmia database.
- Demonstrated significant improvement in overall performance compared to existing methods.
- The dual-channel feature fusion strategy, incorporating a 2D convolutional network, proved highly effective.
Conclusions:
- The proposed dual-channel feature fusion method offers a substantial advancement in ECG signal processing for arrhythmia classification.
- This approach enhances accuracy and objectivity, holding significant potential for clinical applications.
- The methodology promises to improve the efficiency and precision of patient care in managing cardiovascular diseases.
Abstract:
Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management of CVD. This study introduces an innovative approach to enhancing arrhythmia classification accuracy through advanced Electrocardiogram (ECG) signal processing. We propose a dual-channel feature fusion strategy designed to enhance the precision and objectivity of ECG analysis. Initially, we apply an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and enhanced wavelet thresholding for robust noise reduction. Subsequently, in the primary channel, region of interest features are emphasized using a ResNet-ICBAM network model for feature extraction. In parallel, the secondary channel transforms 1D ECG signals into Gram angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP) representations, which are then subjected to two-dimensional convolutional neural network (2D-CNN) feature extraction. Post-extraction, the features from both channels are fused and classified. When evaluated on the MIT-BIH database, our method achieves a classification accuracy of 97.80%. Compared to other methods, our approach of two-channel feature fusion has a significant improvement in overall performance by adding a 2D convolutional network. This methodology represents a substantial advancement in ECG signal processing, offering significant potential for clinical applications and improving patient care efficiency and accuracy.
