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.
Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals
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STDA-Net achieves 89.03% average accuracy and 87.64% macro F1 in cross-dataset sleep staging by processing 2D spectrograms with temporal modeling and unsupervised adversarial alignment, outperforming 1D baselines with lower variance.
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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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STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification
STDA-Net achieves 89.03% average accuracy and 87.64% macro F1 in cross-dataset sleep staging by processing 2D spectrograms with temporal modeling and unsupervised adversarial alignment, outperforming 1D baselines with lower variance.