Key result
ECG-based biometric recognition using SVM achieves ~94% accuracy, outperforming ANN and KNN classifiers.
Absolute Event Rate: 93.709% vs 92.453%
An SVM-based machine learning approach using ECG time-domain features achieves high accuracy (93.7%) for biometric person identification.
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ECG biometrics merit pilot testing in cardiac workflows; leaves open large-scale clinical validation and security assessments.
Patro et al. (2017) studied Biometric recognition (n=20). Support Vector Machine (SVM) classifier vs. Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) classifiers was evaluated on Overall classification accuracy for biometric identification. An ECG-based biometric recognition system using a Support Vector Machine (SVM) classifier achieved an overall classification accuracy of 93.71%, outperforming ANN and KNN classifiers.
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