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.

翻译：在过去二十年间，大量研究证实了利用心脏信号作为生物特征模态的可行性。本文提出了一种基于心电图信号的医疗系统患者识别新方法。通过卷积神经网络对ECG信号转换图像中的用户进行分类。我们在多个数据库上评估了所提识别系统，从而全面理解其在现实场景中的潜力。以往研究普遍忽视了心血管疾病对通用用户识别的影响。本方法充分考虑了患者的心血管状况，确保所得结果不存在偏差或局限性。此外，经大量实验验证，该方法结果一致可靠，误差率更低且准确率指标更高。这些特性使得该方法成为医疗系统患者识别领域的重要贡献，并具备强劲的实际应用竞争力。