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Classification of electrocardiogram signals with waveform morphological analysis and support vector machines.

Hongqiang Li1, Zhixuan An2, Shasha Zuo3

  • 1Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronics and Information Engineering, Tiangong University, Tianjin, China. lihongqiang@tiangong.edu.cn.

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Summary

This study introduces a new method for classifying electrocardiogram (ECG) signals to diagnose arrhythmia accurately. The novel approach combines time and frequency domain features, achieving high classification accuracy for cardiac conditions.

Keywords:
ECG signalSlope thresholdSupport vector machineWaveform shape analysis

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Electrocardiogram (ECG) signals are crucial for diagnosing cardiac diseases.
  • Accurate arrhythmia classification is vital for automated medical diagnosis.
  • Existing methods may require further optimization for speed and precision.

Purpose of the Study:

  • To develop a novel, accurate, and efficient ECG signal classification method.
  • To combine diverse signal features for improved diagnostic performance.
  • To reduce the time required for ECG signal classification.

Main Methods:

  • ECG signal denoising followed by wavelet packet decomposition.
  • Extraction of frequency-domain features (singular value, max value, std dev) from coefficients.
  • Extraction of time-domain features (RR intervals) using the slope threshold method.
  • Fusion of time and frequency features into a comprehensive feature space.
  • Classification using Support Vector Machine (SVM) with grid search and morphological analysis.

Main Results:

  • The proposed method achieved a classification accuracy of 96.67% on arrhythmia databases.
  • Combination of waveform morphology and frequency domain analysis enhanced accuracy.
  • The approach minimized signal classification time.

Conclusions:

  • The novel multi-feature classification method demonstrates high accuracy and efficiency for ECG-based arrhythmia diagnosis.
  • Combining time and frequency domain features provides a robust approach.
  • This method holds promise for improving automated cardiac disease diagnosis.