Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada.
National Research Council of Canada, Government of Canada, Ottawa, ON K1A 0R6, Canada.
Sensors (Basel). 2023 May 13;23(10):4727. doi: 10.3390/s23104727.
Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.
数字通信的进步使得远程患者访问和病情监测成为可能，这要归功于数字医疗系统的革命。基于上下文信息的连续认证相对于传统认证具有许多优势，包括能够持续估计用户在整个会话期间是其声称的用户的可能性，这使得它成为主动调节对敏感数据的授权访问的更有效安全措施。当前依赖机器学习的认证模型存在其缺点，例如向系统注册新用户或模型训练对不平衡数据集的敏感性存在困难。为了解决这些问题，我们建议使用数字医疗系统中易于访问的 ECG 信号，通过能够处理 ECG 信号微小变化的 Ensemble Siamese Network (ESN) 进行身份验证。为该模型添加特征提取预处理可以获得更好的结果。我们在 ECG-ID 和 PTB 基准数据集上训练了这个模型，分别达到了 93.6%和 96.8%的准确率，以及 1.76%和 1.69%的等错误率。数据可用性、简单性和鲁棒性的结合使其成为智能医疗和远程医疗的理想选择。
