用于自动识别收缩和运动工件的多通道EHG分割

Amer Zaylaa, Ahmad Diab, M. Khalil, C. Marque
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引用次数: 8

摘要

在最近的过去,已经发展了几种技术来分析包含在电孕相信号(EHG)中的事件。但是,他们中的大多数人都专注于线下方法。在这项研究中,我们使用了一种在线方法,这种方法是以前开发的,并被称为动态累积和(DCS)。通过4×4电极矩阵将该方法应用于实际eeg信号数据库。为此,首先调整影响DCS方法的两个参数,分析窗口的大小和功能检测阈值,以便通过比较相同标记的信号,从EHG信号中识别出破裂检测的推荐值。对收缩提取的数据进行数据融合,提取收缩检测性能和电极上的性能进行分析。根据所获得的结果,DCS似乎是一种令人鼓舞的方法,可以用于自动断裂分割。然而,将融合方法应用于16个电极上的DCS获得的数据还需要进一步的努力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multichannel EHG segmentation for automatically identifying contractions and motion artifacts
In a recent past, several techniques have been developed to analyze the events contained in the electrohysterogram signals (EHG). But, the most of them focused on offline methods. In this study, we use an online method which is developed previously and known by Dynamic Cumulative Sum (DCS). The approach is applied on real EHG signals database through a 4×4 electrodes matrix. For this purpose, two parameters affecting the DCS method, the size of analyzing window and the function detection thresholding value, were first tuned in order to identify the recommended values for ruptures detection from the EHG signals by comparing to the same labeled signals. Furthermore, data fusion of the contraction extracted data has been applied and then analyzed by extracting the contractions detection performance and the performance on electrodes. According to the obtained results, DCS seems to be an encouraging method that could be used for automatic ruptures segmentation. However, further efforts are needed to apply fusion methods to the obtained data from DCS applied on the sixteen electrodes.
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