Electrocardiogram based Identification Using a New Effective Intelligent Selection of Fused Features

Over the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, andsome methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals hasbeen proposed. This method is developed in such a way that it is able to select important features that are necessary for identificationusing analysis of the ECG signals. For this purpose, after ECG signal preprocessing, its characterizing features were extracted andthen compressed using the cosine transform. The more effective features in the identification, among the characterizing features, areselected using a combination of the genetic algorithm and artificial neural networks. The proposed method was tested on three publicECG databases, namely, MIT‑BIH Arrhythmias Database, MITBIH Normal Sinus Rhythm Database and The European ST‑T Database,in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias.Identification rates of 99.89% and 99.84% and 99.99% are obtained for these databases respectively. The proposed algorithm exhibitsremarkable identification accuracies not only with normal ECG signals, but also in the presence of various arrhythmias. Simulationresults showed that the proposed method despite the low number of selected features has a high performance in identification task.