ECG-based personal identification using empirical mode decomposition and Hilbert transform

Security concerns give biometrics an important role in security solutions. There is strong evidence that we can use electrocardiogram (ECG) signals to identify individuals. In other words, they contain sufficient discriminative information to allow the identification of individuals from a large population. Therefore, this paper presents an individual identification system using 20 healthy subjects from Physikalisch-Technische Bundesanstalt (PTB) database. In this way, Empirical mode decomposition (EMD) is used to decompose our signals to their base component. Then, the instantaneous frequency of the last component is computed by using Hilbert transform. Finally, 1 nearest neighbor (1NN) classifier is utilized to identify the accuracy of our method. With this procedure, we obtained a high identification rate (93.22%).