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ECG classification system based on multi-domain features approach coupled with least square support vector machine

Russel R Majeed1, Sarmad K D Alkhafaji1

  • 1College of Education for Pure Sciences, University of Thi-Qar, Nasiriyah, Iraq.

Computer Methods in Biomechanics and Biomedical Engineering
|May 13, 2022
PubMed
Summary

Electrocardiogram (ECG) biometrics offer a secure alternative to passwords for device authentication. This study introduces a novel ECG verification model using multi-domain features and a Least Square Support Vector Machine (LS-SVM) for high accuracy.

Keywords:
ECGLS-SVMauthenticationfrequency featurestime domain features

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Area of Science:

  • Biometrics and Security Engineering
  • Signal Processing and Machine Learning

Background:

  • Traditional password authentication methods exhibit significant weaknesses in speed and integrity.
  • Biometric authentication, particularly using electrocardiogram (ECG) signals, is gaining prominence due to its unique and difficult-to-counter nature.

Purpose of the Study:

  • To develop a robust ECG-based authentication system.
  • To investigate the effectiveness of multi-domain features for ECG verification.
  • To compare the performance of different classifiers for ECG authentication.

Main Methods:

  • Extraction of time and frequency domain features from ECG signals using an optimized Triple Band filter bank.
  • Feature selection to identify the most relevant and non-redundant features.
  • Classification using Least Square Support Vector Machine (LS-SVM), K-means, and K-nearest algorithms.

Main Results:

  • The proposed ECG biometric authentication system demonstrated superior performance compared to existing methods.
  • Individual feature domains achieved accuracies of 88% (time) and 95% (frequency).
  • A combination of time and frequency features yielded an outstanding accuracy of 99%.

Conclusions:

  • The proposed multi-domain feature extraction coupled with LS-SVM provides a highly accurate and robust ECG biometric authentication solution.
  • Combining time and frequency domain features significantly enhances the performance of ECG-based verification systems.
  • The developed model shows great potential for securing device data through reliable biometric identification.