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Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine.

Weiyi Yang1, Yujuan Si2, Di Wang1

  • 1College of Communication Engineering, Jilin University, Changchun, 130012, China.

Computers in Biology and Medicine
|August 12, 2018
PubMed
Summary

This study introduces a new method for electrocardiogram (ECG) classification using Principal Component Analysis Network (PCANet) and Support Vector Machine (SVM) to accurately identify arrhythmias, even with noisy and imbalanced data.

Keywords:
Arrhythmia recognitionCardiovascular diseasesDeep learningNoise robustnessPrincipal component analysis network

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning in Healthcare

Background:

  • Electrocardiogram (ECG) classification is crucial for arrhythmia identification.
  • Existing neural network models struggle with heartbeat noise and imbalanced ECG data.
  • Robust and accurate ECG analysis methods are needed.

Purpose of the Study:

  • To develop a novel heartbeat recognition method for noisy and skewed ECG signals.
  • To enhance the accuracy and efficiency of arrhythmia detection.
  • To address limitations of current ECG classification techniques.

Main Methods:

  • Utilized Principal Component Analysis Network (PCANet) for effective feature extraction from noisy ECG signals.
  • Employed a linear Support Vector Machine (SVM) to improve classification speed.
  • Validated the method on five types of imbalanced ECGs from the MIT-BIH arrhythmia database.

Main Results:

  • Achieved high recognition accuracy of 97.77% on original ECGs and 97.08% on noise-free ECGs.
  • Demonstrated superior performance in classifying skewed and noisy heartbeats.
  • The proposed method shows significant noise robustness and applicability to imbalanced datasets.

Conclusions:

  • The presented PCANet-SVM method offers a practical and effective solution for ECG recognition.
  • The approach exhibits excellent noise robustness and skewed data applicability.
  • This method holds promise for improving automated arrhythmia detection systems.