Electrocardiogram authentication method robust to dynamic morphological conditions

This study proposes a human authentication framework based on electrocardiogram signals that are robust to dynamic cardiac morphological conditions. The proposed method incorporates a stationary wavelet transform, an infinite feature selection, and a linear discriminant analysis. Evaluation experiments were conducted under three modulated situations: temporal variation, postural variation, and heart rate variation when exercising. Compared with three state-of-the-art methods, the performance of the proposed method was shown to be better overall, with an equal error rate (EER) of 1.48% under time-varying situations, 1.74% under posture changes, and 5.47% after exercise. These results indicate that the proposed method achieves a highly increased performance compared with state-of-the-art techniques. Further evaluation of the identification performance of the proposed method on two public databases shows that it performs better than previously proposed methods.