基于心电图进行身份验证的云服务

Yu. O. Luhovskyi
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引用次数: 0

摘要

远程医疗的发展速度每年都在加快。在信息技术的范围内,已经创造了新的技术来解决信息通信问题。其中之一是远程监测,需要进行心电图分析。在这种情况下,重要的是要确认通过信息通道传递的心电图属于患者。来自不同国家的研究人员致力于这个问题。他们提出了不同的心电图认证方法。这项工作的目标是提出一个服务的原型,可以用来验证心电图。本文描述了用于构建认证技术的算法及其实现方法。构建应用程序的历史很短。它显示了它们的结构和目的。该系统是认证服务的雏形。它执行新心电图的注册,这是认证中最耗时的过程。文中介绍了系统的结构,并给出了实验结果。结果表明,机器学习库ML.NET存在性能问题。当大量的核心被分配到一个机器学习实例时,开销大大减少了总体实验时间。这些实验证实了阿姆达尔定律。尽管如此,我们还是找到了一个体系结构,在这个体系结构中,实验的执行时间最短。了解了机器学习库的问题,建议了一个新的架构设置,并将在未来的工作中实现。此外,还关注了发达的服务应如何帮助研究人员改进技术。该系统的主要思想是使用一个服务来开发和测试该技术。这意味着我们可以确保在生产环境中运行的云服务的行为与研究阶段相同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud service for authentication of a person based on their electrocardiogram
Telemedicine grows faster with each year. In scope of it new technologies have been created to solve information communication problems. One of them is distance monitoring which requires electrocardiogram analysis. In this case it is important to confirm that the transferred electrocardiogram through information channels belongs to the patient. Researchers from different countries work on this problem. They suggest different methods of authentication by electrocardiogram. The goal of this work is to suggest a prototype of a service that could be used to authenticate electrocardiograms. The paper describes which algorithms have been used to build authentication technology and how it was implemented. There is a short history of built applications. It shows their structures and the purpose. The recently developed system is a prototype to authentication service. It performs registration of new electrocardiograms which is the most time consuming process in authentication. The paper describes the architecture of the system and shows the result of executed experiments. The results show that there is a performance issue with the machine learning library ML.NET. When a lot of cores are allocated to one machine learning instance the overheads highly decrease the overall experiment time. These experiments confirmed Amdahl’s law. Nevertheless, an architecture was found where experiments took the least time for execution. Knowing the issue with the machine learning library, a new architecture setup was suggested and will be implemented in future works. Besides, attention is paid on how developed service should help researchers to improve the technology. The main idea of the system is using one service for developing and testing the technology. That means we can be sure that the cloud service that runs on production would behave the same as in the research phase.
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