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用于癫痫患者监测的多模态流数据实时管理

Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients.

作者信息

Triantafyllopoulos Dimitrios, Korvesis Panagiotis, Mporas Iosif, Megalooikonomou Vasileios

机构信息

Multidimensional Data Analysis and Knowledge Management Laboratory, Department of Computer Engineering and Informatics, University of Patras, 26500, Rion-Patras, Greece.

出版信息

J Med Syst. 2016 Mar;40(3):45. doi: 10.1007/s10916-015-0403-3. Epub 2015 Dec 7.

Abstract

New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patient's physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data.

摘要

新一代医疗保健以可穿戴健康监测系统为代表,该系统可对患者的生理参数进行实时监测。预计对生命信号进行连续动态监测将改善患者的治疗效果,并实现主动的个人健康管理。在本文中,我们展示了一种用于癫痫管理的多模态实时系统的实现。所提出的方法基于一种数据流架构以及对大量生理参数的高效管理。针对所记录数据的不同空间分辨率,对该架构的性能进行了检验。

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