  Over the course of the past two decades, a substantial body of research has
substantiated the viability of utilising cardiac signals as a biometric
modality. This paper presents a novel approach for patient identification in
healthcare systems using electrocardiogram signals. A convolutional neural
network is used to classify users based on images extracted from ECG signals.
The proposed identification system is evaluated in multiple databases,
providing a comprehensive understanding of its potential in real-world
scenarios. The impact of Cardiovascular Diseases on generic user identification
has been largely overlooked in previous studies. The presented method takes
into account the cardiovascular condition of the patients, ensuring that the
results obtained are not biased or limited. Furthermore, the results obtained
are consistent and reliable, with lower error rates and higher accuracy
metrics, as demonstrated through extensive experimentation. All these features
make the proposed method a valuable contribution to the field of patient
identification in healthcare systems, and make it a strong contender for
practical applications.
