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CDLR-net: a ECG classification network based on deep residual shrinkage networks and LSTM
Zhanhang Qiu1, Suigu Tang2, Huazhu Liu1,2
1School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China.
Computer Methods in Biomechanics and Biomedical Engineering
|September 7, 2025
Summary
This study introduces CDLR-Net, a novel deep learning model for accurate electrocardiogram (ECG) analysis. By integrating RR interval data with ECG signals, it significantly improves premature heartbeat detection.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence in Healthcare
Background:
- Traditional electrocardiogram (ECG) classification networks often rely solely on MLII lead signals.
- This approach faces accuracy limitations, particularly for subtle ECG features like premature heartbeats.
Purpose of the Study:
- To develop an advanced ECG classification network, CDLR-Net, enhancing accuracy for premature heartbeats.
- To integrate both MLII ECG signals and RR interval features for improved diagnostic performance.
Main Methods:
- A novel CDLR-Net combining Deep Residual Shrinkage Network (DRSN) and Long Short-Term Memory (LSTM).
- Wavelet decomposition for ECG signal denoising.
- Extraction of various RR interval features (pre-RR, post-RR, local-10 average RR, overall average RR) post R-wave localization.
Main Results:
- Achieved 97% accuracy in inter-patient classification on the MIT-BIH database.
- Attained 99% accuracy in intra-patient classification on the MIT-BIH database.
- Demonstrated significant improvement in ECG classification accuracy by incorporating RR interval information.
Conclusions:
- The proposed CDLR-Net effectively enhances ECG classification accuracy, especially for premature heartbeats.
- Integration of RR interval features alongside MLII ECG signals is a promising strategy for robust cardiac analysis.
- The CDLR-Net shows high potential for clinical application in automated ECG interpretation.
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