Key result
An artificial neural network classifier using extracted P-QRS-T features from ECG signals achieved a total accuracy of 95.245% for biometric recognition.
Machine learning classification of ECG signal features can effectively identify individuals for biometric purposes with high accuracy.
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Supports ECG biometrics feasibility; leaves open prospective validation before clinical or security use.
Kuila et al. (2020) studied Biometric recognition (n=47). Machine learning classification (ANN) was evaluated on Total Accuracy. An artificial neural network classifier using extracted P-QRS-T features from ECG signals achieved a total accuracy of 95.245% for biometric recognition.
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