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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
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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.

Keywords:
Arrhythmia detectionDRSNLSTMRR interval

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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.