Suppr超能文献

睡眠期间EMFi薄片中尖峰事件的自动检测。

Automatic detection of spiking events in EMFi sheet during sleep.

作者信息

Alametsä Jarmo, Rauhala Esa, Huupponen Eero, Saastamoinen Antti, Värri Alpo, Joutsen Atte, Hasan Joel, Himanen Sari-Leena

机构信息

Digital Media Institute, Tampere University of Technology, Signal Processing Laboratory, Korkeakoulunkatu 1, FIN-33101, Tampere, Finland.

出版信息

Med Eng Phys. 2006 Apr;28(3):267-75. doi: 10.1016/j.medengphy.2005.07.008. Epub 2005 Aug 16.

Abstract

In this paper we present a new method for detection of spiking events caused by the increased respiratory resistance (IRR) from ballistocardiographic (BCG) data recorded with EMFi sheet. Spiking is a phenomenon where BCG wave complexes increase in amplitude during IRR. In this study data from six patients with a total of 1503 visually scored spiking events were studied. The algorithm monitors amplitude levels of BCG complexes and detects large relative increases. In this work 10 different variations of the algorithm were compared in order to find the best variation, which can cope with different recordings. The best variation of the algorithm was able to detect spiking events with 80% true positive and 19% false positive rates. The detection is not dependent on absolute waveform amplitudes and therefore does not require any recording-specific tuning prior to application. It is important to recognize spiking events in order to evaluate the severity of respiratory disturbance during sleep.

摘要

在本文中,我们提出了一种新方法,用于从用EMFi薄片记录的心冲击图(BCG)数据中检测由呼吸阻力增加(IRR)引起的尖峰事件。尖峰是指在IRR期间BCG波复合体振幅增加的现象。在本研究中,对6名患者的数据进行了研究,共计1503个经视觉评分的尖峰事件。该算法监测BCG复合体的振幅水平,并检测大幅相对增加。在这项工作中,比较了该算法的10种不同变体,以找到能够应对不同记录的最佳变体。该算法的最佳变体能够以80%的真阳性率和19%的假阳性率检测尖峰事件。该检测不依赖于绝对波形振幅,因此在应用前不需要任何特定记录的调整。识别尖峰事件对于评估睡眠期间呼吸紊乱的严重程度很重要。

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