An ECG Biometric System Based on Empirical Mode Decomposition and Hilbert-Huang Transform for Improved Feature Extraction

In this paper, we propose an ECG biometric system based on instantaneous frequencies extracted by the Hilbert-Huang Transform (HHT). The system was evaluated using two different databases: PTB Diagnostic ECG and MIT-BIH Arrhythmia. The results demonstrate the robustness of our system, as we achieved a testing accuracy of 95.31% for the MIT-BIH database and 96.42% for the PTB database. For the classification, we used a deep learning model based on 3 gated recurrent units (GRU) cells. This paper aims to verify the efficacy of using instantaneous frequencies as a feature for biometric identification.