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Electrocardiogram01:29

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Related Experiment Video

Updated: Sep 24, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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[Electrocardiogram signal classification algorithm of nested long short-term memory network based on focal loss

Shiyu Xu1, Site Mo1, Huijun Yan1

  • 1School of Electrical Engineering, Sichuan University, Chengdu 610065, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|May 6, 2022
PubMed
Summary

This study introduces a novel nested long short-term memory network (NLSTM) to improve arrhythmia classification in electrocardiogram (ECG) data. The NLSTM model effectively addresses label imbalance and enhances recognition of minority samples, achieving 98.34% accuracy.

Keywords:
ArrhythmiaFocal lossNested long short-term memory networkResidual attentionSynthetic minority over-sampling technique

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Electrocardiogram (ECG) signals are crucial for diagnosing heart arrhythmias.
  • Label imbalance in ECG datasets negatively impacts arrhythmia classification accuracy.
  • Existing methods struggle with complex temporal characteristics and imbalanced sample distributions.

Purpose of the Study:

  • To develop an effective model for unbalanced ECG signal classification.
  • To improve the accuracy of arrhythmia detection and classification.
  • To provide a feasible method for ECG-assisted diagnosis.

Main Methods:

  • Proposed a nested long short-term memory network (NLSTM) model.
  • Utilized focal loss function to down-weight easily classified samples.
  • Implemented a residual attention mechanism to address sample imbalance.
  • Applied synthetic minority over-sampling technique (SMOTE) to the MIT-BIH-AR database.

Main Results:

  • The NLSTM model achieved an overall accuracy of 98.34% on the MIT-BIH arrhythmia database.
  • Effectively addressed issues of imbalanced samples and unremarkable features in ECG signals.
  • Significantly improved the recognition and classification of minority arrhythmia samples.

Conclusions:

  • The proposed NLSTM model offers a robust solution for unbalanced ECG signal classification.
  • Demonstrates practical application significance for computer-aided diagnosis of cardiac arrhythmias.
  • Highlights the potential of deep learning with attention mechanisms in analyzing complex biomedical signals.