Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA.
Sustainable Management Strategy, Korea Expressway Corporation, Gimcheon 39660, Korea.
Sensors (Basel). 2017 Jun 12;17(6):1360. doi: 10.3390/s17061360.
Long-term electrocardiogram (ECG) monitoring, as a representative application of cyber-physical systems, facilitates the early detection of arrhythmia. A considerable number of previous studies has explored monitoring techniques and the automated analysis of sensing data. However, ensuring patient privacy or confidentiality has not been a primary concern in ECG monitoring. First, we propose an intelligent heart monitoring system, which involves a patient-worn ECG sensor (e.g., a smartphone) and a remote monitoring station, as well as a decision support server that interconnects these components. The decision support server analyzes the heart activity, using the Pan-Tompkins algorithm to detect heartbeats and a decision tree to classify them. Our system protects sensing data and user privacy, which is an essential attribute of dependability, by adopting signal scrambling and anonymous identity schemes. We also employ a public key cryptosystem to enable secure communication between the entities. Simulations using data from the MIT-BIH arrhythmia database demonstrate that our system achieves a 95.74% success rate in heartbeat detection and almost a 96.63% accuracy in heartbeat classification, while successfully preserving privacy and securing communications among the involved entities.
长期心电图（ECG）监测，作为一个代表网络物理系统的应用，有助于早期发现心律失常。以前的大量研究都探讨了监测技术和对传感数据的自动分析。然而，在 ECG 监测中，确保患者隐私或机密性并不是主要关注点。首先，我们提出了一个智能心脏监测系统，该系统涉及一个患者佩戴的心电图传感器（例如智能手机）和一个远程监测站，以及一个将这些组件互连的决策支持服务器。决策支持服务器使用 Pan-Tompkins 算法来检测心跳，并使用决策树对其进行分类，从而分析心脏活动。我们的系统通过采用信号扰乱和匿名身份方案来保护传感数据和用户隐私，这是可靠性的一个重要属性。我们还使用公钥密码系统来实现实体之间的安全通信。使用麻省理工学院-贝斯以色列医院心律失常数据库中的数据进行的模拟表明，我们的系统在心跳检测方面的成功率达到 95.74%，在心跳分类方面的准确率几乎达到 96.63%，同时成功保护了所涉及实体之间的隐私和安全通信。
