ECG-biometrics-bench standardizes evaluation to expose the Random Split Fallacy, where intra-session splits inflate ECG biometric performance, revealing temporal drift degradation that is not model-specific and can be partially mitigated by multi-session template fusion.
Empirical mode decomposition-based biometric identification using gru and lstm deep neural networks on ecg signals,
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A hybrid 1D-2D CNN with attention-guided fusion reports over 99% identification accuracy on three ECG datasets and moderate cross-session stability on a ten-year multi-session dataset.
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ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics
ECG-biometrics-bench standardizes evaluation to expose the Random Split Fallacy, where intra-session splits inflate ECG biometric performance, revealing temporal drift degradation that is not model-specific and can be partially mitigated by multi-session template fusion.
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Attention-Guided Fusion of 1D and 2D CNNs for Robust ECG-Based Biometric Recognition
A hybrid 1D-2D CNN with attention-guided fusion reports over 99% identification accuracy on three ECG datasets and moderate cross-session stability on a ten-year multi-session dataset.