Heart biometrics based on ECG signal by sparse coding and bidirectional long short-term memory

Physiological signal-based biometrics are gaining increasing attention in the context of increasing privacy and security requirements. This paper proposes a novel electrocardiogram (ECG)-based algorithm to be used for human identification by integrating multiple local feature vectors with sparse-constraint-based sparse coding (SCSC) and bidirectional long short-term memory (BLSTM). Three local feature vectors of ECG signals: morphology characteristics in the time domain, instantaneous characteristics in the frequency domain, and phase spectral characteristics in the phase domain are constructed. Sparsity constraints to model this relationship are imposed because ECGs show high inter-class similarity and subtle intra-class differences in these three domains, and traditional sparse coding (SC) can only learn from a single dictionary. This paper joints optimization of the summed reconstruction error, the sparsity constraints of the correlations and the differences between the feature vectors, proposed the SCSC algorithm. Via this approach, the overlap problem of local feature vectors is solved and a lightweight and interpretable feature vector is obtained. Additionally, the BLSTM-based deep neural network model is supplemented for exploring the spatial information of the reconstructed feature vectors, and a more representative and discriminative signal feature representation is obtained. Comparing five classical machine learning and deep learning algorithms within 360 public samples, using two protocols, we show that, in addition to multiscale information extraction, joint encoding of the correlations and differences between local feature vectors is critically important for feature discrimination. The experimental results demonstrated a high identification accuracy of 99.44%, indicating that the proposed algorithm has practical utility in network information security.