Evaluation of Efficient Electrocardiomatrix-Based Identification Using Deep Learning Methods

Nowadays, biometric systems are essential for identification and authentication. This study proposes deep learning models for user identification using electrocardiomatrices (ECMs) as input. The proposed models are evaluated from different perspectives to assess their efficiency and performance for user identification. One aspect involves comparing convolutional and recurrent neural models. In contrast, another element consists of testing the models with different numbers of ECMs and beats per frame for generating the ECMs. The results are based on an analysis of three different electrocardiogram databases.