Among the information security problems involved in Telemedicine Information Systems (TMIS), the authentication area of the various entities involved has been extensively discussed in recent years and shown a wide range of possibilities. The problems caused by the application of inadequate authentication processes may lead to the death of patients who depend on Mobile Healthcare (M-Health) services. User authentication can be based on several physiological traits (e.g., iris, retina, and fingerprint) for biometric recognition, including electrocardiography (ECG) signals. Some ECG patterns are relatively robust to daily changes associated with normal heart rate variability. In fact, the relative lengths of PQ, QR, and RS intervals, as well as Q, R, and S relative amplitudes constitute individual traits. A few studies have succeeded in using ECG signals as an accurate authentication input and offered some advantages in comparison to biometrics traditional approaches. ECG-based user authentication can be built on Machine Learning (ML) models, used for classification purposes and reductions in distortions caused by misinterpretation of ECG data. Among the ML models adopted for ECG signals classification, ensembles have shown a good research opportunity. Random Under-Sampling Boosting (RUSBoost), a boosting algorithm not yet explored (to the best of our knowledge) for such a problem, can achieve comparatively high performance after a supervised training stage, even from relatively few training examples. This manuscript reports on a comparison of RUSBoost with Nearest Neighbour Search (NNS) regarding the classification of ECG signals for biometric authentication applications. The two ML techniques were compared by a random subsampling technique that considers four analysis metrics, namely accuracy, precision, sensitivity, and F1-score. The experimental results showed the better performance of RUSBoost regarding accuracy (97.4%), sensitivity (96.1%) and F1-score (97.4%). On the other hand, NNS provided better precision (99.5%).

在远程医疗信息系统（TMIS）涉及的信息安全问题中，涉及的各个实体的认证领域近年来被广泛讨论，并显示出广泛的可能性。应用不充分的身份验证流程所导致的问题可能会导致依赖移动医疗 (M-Health) 服务的患者死亡。用户身份验证可以基于多种生理特征（例如，虹膜、视网膜和指纹）进行生物识别，包括心电图 (ECG) 信号。一些 ECG 模式对与正常心率变异性相关的日常变化相对稳健。事实上，PQ、QR和RS区间的相对长度，以及Q、R和S相对幅度构成了个体特征。一些研究成功地将 ECG 信号用作准确的身份验证输入，并且与生物识别传统方法相比具有一些优势。基于 ECG 的用户身份验证可以建立在机器学习 (ML) 模型上，用于分类目的和减少由 ECG 数据误解引起的失真。在用于 ECG 信号分类的 ML 模型中，集成显示了良好的研究机会。随机欠采样提升 (RUSBoost) 是一种尚未针对此类问题探索过的提升算法（据我们所知），即使从相对较少的训练示例中，也可以在监督训练阶段后获得相对较高的性能。这份手稿报告了 RUSBoost 与最近邻搜索 (NNS) 在用于生物特征认证应用的 ECG 信号分类方面的比较。两种 ML 技术通过随机子采样技术进行比较，该技术考虑了四个分析指标，即准确度、精确度、灵敏度和 F1 分数。实验结果表明，RUSBoost 在准确度 (97.4%)、灵敏度 (96.1%) 和 F1-score (97.4%) 方面的性能更好。另一方面，NNS 提供了更好的精度（99.5%）。