Sajjad Maleki ¹ Akram Beigi ¹ Nasour Bagheri ^1* Pedro Peris ² Carmen Camara ²

• ¹ Shahid Rajaee Teacher Training University, Tehran, Iran
• ² Universidad Carlos III de Madrid de Madrid, Leganés, Spain

Identity verification, along with the design and implementation of robust automatic authentication systems, is a pivotal concern in contemporary society. A spectrum of authentication methodologies exists, encompassing software, hardware, and biometric modalities. Biometric modalities have garnered substantial scholarly interest due to their accuracy and resilience against falsification attempts. This study presents an identity verification framework leveraging electrocardiogram (ECG) signals. Notable cardiac signal repositories, including the NSRDB and MITDB datasets, are available for researchers to evaluate the system. However, these repositories are characterized by noise, necessitating preprocessing procedures. In the proposed framework, signal data undergo initial cleansing followed by transformation into the frequency domain to facilitate feature extraction. Using the Wigner-Ville distribution, ECG signals are converted into image data, with each resultant image encapsulating an individual's cardiac signal information and exhibiting uniqueness in a noise-free environment. The framework employs deep learning methodologies and convolutional neural networks (CNNs) for data recognition. Given the inherent characteristics of ECG signals, the GoogleNet architecture is utilized for training, testing, and image classification.The model derived from this framework provides individual identification. Ultimately, this study achieves an accuracy of 99.3% and an Equal Error Rate (EER) of 0.8% for NSRDB, as well as an accuracy of 99.004% with an EER of 0.8% for MITDB, demonstrating superior precision compared to alternative methodologies.