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Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent
Gyu Ho Choi1, Hoon Ko1, Witold Pedrycz2
1IT Research Institute, Chosun University, Gwangju 61452, Korea.
Sensors (Basel, Switzerland)
|December 16, 2020
Summary
This study introduces a novel time and frequency fusion normalization method to improve electrocardiogram (ECG) biometrics. The technique enhances user recognition accuracy by matching pre- and post-exercise ECG signals effectively.
Area of Science:
- Biometrics
- Cardiovascular Signal Processing
Background:
- Electrocardiogram (ECG) biometrics face challenges due to environmental variations.
- Post-exercise ECG signals often differ morphologically from pre-exercise signals, impacting recognition accuracy.
Purpose of the Study:
- To develop a robust method for matching pre- and post-exercise ECG cycles.
- To enhance user recognition performance in ECG-based biometrics.
Main Methods:
- Proposed a time and frequency fusion normalization method for ECG signal matching.
- Utilized linear interpolation and optimized frequency filtering for normalization.
- Focused on preserving morphological features like P wave, QRS complexes, and T wave.
Main Results:
- Achieved a 25.6% average improvement in similarity between pre- and post-exercise ECG states.
- Enhanced maximum user recognition performance from 96.4% to 98% for 30 ECG cycles.
Conclusions:
- The proposed fusion normalization method effectively addresses ECG signal variations.
- This technique significantly improves the reliability and accuracy of ECG biometrics.
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