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：过去二十年间，大量研究证实了心脏信号作为生物特征模态的可行性。本文提出一种基于心电图信号的新型医疗系统患者识别方法。该方法采用卷积神经网络对心电信号图像进行分类以识别用户身份。通过多数据库验证所提识别系统的性能，全面评估其在实际场景中的应用潜力。此前研究普遍忽视了心血管疾病对通用用户识别的影响，本文方法将患者心血管状况纳入考量，确保所得结果无偏倚且无局限性。大量实验结果表明，该方法具有较低错误率和较高准确率，结果稳定可靠。这些特性使所提方法成为医疗系统患者识别领域的重要贡献，并为实际应用提供有力支撑。