Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals

This paper is concerned with personal identification using a robust EigenECG network (REECGNet) based on time-frequency representations of electrocardiogram (ECG) signals. For this purpose, we use a robust principal component analysis network (RPCANet) and wavelet analysis. In general, PCA performance and applicability in real case scenarios is limited by the lack of robustness to outliers and corrupted observations. However, in a real nonstationary ECG noise environment, RPCA shows good performance when the method is applied with variable dimensions of local signal subspaces. That is why RPCA-based ECG identification is extremely robust with nonlinear data. Also, a REECGNet performs well without back-propagation to obtain features from the visual content. We constructed a Chosun University ECG Database (CU-ECG DB) and compared with the Physikalisch-Technische Bundesanstalt ECG database (PTB-ECG DB), which is shared data. Finally, the experimental results show the advantages and effectiveness of the applied recognition scheme with 98.25% performance. In addition, to demonstrate the superiority of REECGNet, we experimented with adding noise and the experimental result showed 97.5% recognition rate.