Suppr超能文献

网格技术在睡眠呼吸障碍自动检测中的应用。

Application of Grid technology for automated detection of sleep disordered breathing.

作者信息

Canisius Sebastian, Penzel Thomas, Kesper Karl, Krefting Dagmar

机构信息

Philipps-University Marburg, Faculty of Medicine, Department for Internal Medicine, Marburg, Germany.

出版信息

Stud Health Technol Inform. 2009;147:22-30.

Abstract

Sleep related breathing disorders (SRBD) represent a major disease in sleep medicine. For diagnosis and therapy control, extensive overnight investigations are required, encompassing long-term measurement of multiple biosignals in specialized sleep disorders centers. To date, evaluation of the examination is realized by comprehensive visual inspection of the data by an expert. Therefore, many approaches have been made to facilitate diagnosis, among them automated analysis of the ECG signal. In this article, we present a grid based infrastructure for computer aided diagnosis of SRBD, accessible for distributed users. As the analysis algorithms itself are still in a validation phase, and the Grid infrastructure is not approved for clinical applications, the application is currently used for research purposes only. But as important aspects of data-security, accessibility from protected environments, usability and fault-tolerance are already covered, the implementation is a solid base for further enhancement of the platform and paves the way for a sustainable service grid for sleep medicine.

摘要

睡眠相关呼吸障碍(SRBD)是睡眠医学中的一种主要疾病。为了进行诊断和治疗控制,需要进行广泛的夜间检查,包括在专门的睡眠障碍中心对多个生物信号进行长期测量。迄今为止,检查评估是由专家对数据进行全面的目视检查来实现的。因此,已经采取了许多方法来促进诊断,其中包括对心电图信号的自动分析。在本文中,我们提出了一种基于网格的基础设施,用于SRBD的计算机辅助诊断,可供分布式用户使用。由于分析算法本身仍处于验证阶段,且网格基础设施尚未获批用于临床应用,该应用目前仅用于研究目的。但由于数据安全、从受保护环境的可访问性、可用性和容错性等重要方面已经得到涵盖,该实现为平台的进一步增强奠定了坚实基础,并为睡眠医学的可持续服务网格铺平了道路。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验