Person Identification from Synthesized ECG Signal

The electrocardiogram (ECG) based biometric system has been gaining wide attention in recent years. ECG has many attractive characteristics for human identification applications, such as it is easily measured, universal, difficult to counterfeit, and offers additional information on physiological and clinical traits. In this paper, we attempt to analyse the effectiveness of the synthesised ECG signal for person identification applications. This paper also investigates the similarity of the biometric information present in the original signals and their reconstructed signals of the same record. We have proposed a novel performance measure, i.e., person identifiability (PI), to evaluate the synthesis model. An LSTM based neural network model has been employed to synthesise the ECG leads. The person identification performance for the synthesised ECG leads is obtained using the HLSTM model. The proposed framework is tested using the PTB ECG database.