Electrocardiograms (ECG) directly measure cardiac electrical activity, while Photoplethysmography (PPG) indirectly reflects cardiac function; jointly, they reveal vital information about heart health. This paper pioneers the challenges of fusing ECG and PPG signals for identity authentication. We introduce an innovative approach that combines features from both ECG and PPG signals using Singular Spectrum Analysis (SSA) and a feature fusion model. Compared to existing methodologies, our approach demonstrates enhanced accuracy and robustness in identity verification. The results indicate a significant improvement in authentication precision using this integrated method. Simulated studies validate the effectiveness of our approach, underscoring its potential applications in the realm of identity authentication.
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Liu et al. (2024) studied this question.
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