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Noise robust automatic heartbeat classification system using support vector machine and conditional spectral moment.

Pratik Singh1, Gayadhar Pradhan2

  • 1Department of Electronics and Communication Engineering, National Institute of Technology, Tadepalligudem, Andhra Pradesh, 534101, India. pratik0821@gmail.com.

Physical and Engineering Sciences in Medicine
|November 24, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel noise-robust heartbeat classifier using a conditional spectral moment (CSM) feature. The new method improves arrhythmia detection accuracy by effectively handling noisy signals.

Keywords:
Conditional spectral momentElectrocardiogramHeartbeat classificationMorphological featureSupport vector machine

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Accurate heartbeat classification is crucial for detecting cardiac arrhythmias.
  • Effective classification requires noise-robust features to handle signal variability.
  • Existing methods often struggle with noise, impacting diagnostic reliability.

Purpose of the Study:

  • To propose a novel noise-robust support vector machine (SVM) based heartbeat classifier.
  • To introduce a new noise-robust morphological feature based on conditional spectral moment (CSM).
  • To evaluate the performance of the proposed classifier and feature set, including feature combination strategies.

Main Methods:

  • Developed a noise-robust SVM classifier incorporating a novel conditional spectral moment (CSM) feature.
  • Utilized existing features: RR interval, wavelets, and higher-order statistics (HOS).
  • Employed ensemble learning by combining individual SVMs trained on different feature sets using sum, majority, and product rules.

Main Results:

  • Demonstrated the noise-robustness of the proposed CSM feature.
  • Showcased improved overall performance of the proposed classifier compared to existing systems.
  • Validated the effectiveness of combining temporal and morphological features for enhanced classification.

Conclusions:

  • The proposed CSM feature offers significant noise-robustness for heartbeat classification.
  • Ensemble learning with combined features further boosts classification accuracy.
  • The developed classifier presents a promising advancement for arrhythmia detection systems.