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Sensors 2017, 17(6), 1360;

Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection

Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA
Sustainable Management Strategy, Korea Expressway Corporation, Gimcheon 39660, Korea
Department of Computer Science and Engineering, Hanyang University, Ansan 15588, Korea
Department of Computer and Information Sciences, Fordham University, Bronx, NY 10458, USA
Department of Computer Science and Engineering, Korea University, Seoul 02841, Korea
This paper is an extended version of our paper published in Park, J.; Lee, K.; Kang, K. Intelligent Electrocardiogram Monitoring System for Early Arrhythmia Detection. In Proceedings of the IEEE International Conference on Advanced Information Networking and Applications (AINA 2014), Victoria, BC, Canada, 13–16 June 2016.
Authors to whom correspondence should be addressed.
Received: 11 April 2017 / Revised: 25 May 2017 / Accepted: 7 June 2017 / Published: 12 June 2017
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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. View Full-Text
Keywords: body sensor networks; biomedical computing; electrocardiography; arrhythmia detection; communication system security; privacy of patients body sensor networks; biomedical computing; electrocardiography; arrhythmia detection; communication system security; privacy of patients

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Son, J.; Park, J.; Oh, H.; Bhuiyan, M.Z.A.; Hur, J.; Kang, K. Privacy-Preserving Electrocardiogram Monitoring for Intelligent Arrhythmia Detection. Sensors 2017, 17, 1360.

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