Electrocardiogram Signal Authentication System based on Deep Learning

In the world of digital communication, authentication is an important concern and the need for a safe and secure system increases the necessity of designing authentication systems. To perform authentication, biometric-based approaches are of great interest due to the property of being alive and resistant to forgery. In this study, an authentication system based on heart signal is designed. Due to the process of receiving heart signals, their data usually has a lot of noise. In order to prepare the data, in the proposed system, the heart signals are first cleaned and then transferred to the frequency domain for feature extraction. Also, they are converted into an image by applying the Wigner-Ville distribution, so that each image contains the signal information of each person’s heart and is unique. In the proposed authentication system, these images are used for training and evaluation in a deep convolutional neural network. The output of this system provides the possibility of people’s identification. The data of this study are taken from the NSRDB and MITDB databases, and significant results have been obtained compared to previous studies.